AmazonSageMakerClient (AWS SDK for Java

java.lang.Object
- com.amazonaws.AmazonWebServiceClient
- - com.amazonaws.services.sagemaker.AmazonSageMakerClient

All Implemented Interfaces:

AmazonSageMaker

Direct Known Subclasses:

AmazonSageMakerAsyncClient
```
@ThreadSafe
 @Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AmazonSageMakerClient
extends AmazonWebServiceClient
implements AmazonSageMaker
```
Client for accessing SageMaker. All service calls made using this client are blocking, and will not return until the service call completes.

Provides APIs for creating and managing Amazon SageMaker resources.

Other Resources:
- Amazon SageMaker Developer Guide
- Amazon Augmented AI Runtime API Reference

Field Summary
- Fields inherited from class com.amazonaws.AmazonWebServiceClient
  LOGGING_AWS_REQUEST_METRIC
- Fields inherited from interface com.amazonaws.services.sagemaker.AmazonSageMaker
  ENDPOINT_PREFIX

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`AddAssociationResult`	`addAssociation(AddAssociationRequest request)` Creates an association between the source and the destination.
`AddTagsResult`	`addTags(AddTagsRequest request)` Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
`AssociateTrialComponentResult`	`associateTrialComponent(AssociateTrialComponentRequest request)` Associates a trial component with a trial.
`BatchDescribeModelPackageResult`	`batchDescribeModelPackage(BatchDescribeModelPackageRequest request)` This action batch describes a list of versioned model packages
`static AmazonSageMakerClientBuilder`	`builder()`
`CreateActionResult`	`createAction(CreateActionRequest request)` Creates an action.
`CreateAlgorithmResult`	`createAlgorithm(CreateAlgorithmRequest request)` Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
`CreateAppResult`	`createApp(CreateAppRequest request)` Creates a running app for the specified UserProfile.
`CreateAppImageConfigResult`	`createAppImageConfig(CreateAppImageConfigRequest request)` Creates a configuration for running a SageMaker image as a KernelGateway app.
`CreateArtifactResult`	`createArtifact(CreateArtifactRequest request)` Creates an artifact.
`CreateAutoMLJobResult`	`createAutoMLJob(CreateAutoMLJobRequest request)` Creates an Autopilot job.
`CreateCodeRepositoryResult`	`createCodeRepository(CreateCodeRepositoryRequest request)` Creates a Git repository as a resource in your Amazon SageMaker account.
`CreateCompilationJobResult`	`createCompilationJob(CreateCompilationJobRequest request)` Starts a model compilation job.
`CreateContextResult`	`createContext(CreateContextRequest request)` Creates a context.
`CreateDataQualityJobDefinitionResult`	`createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request)` Creates a definition for a job that monitors data quality and drift.
`CreateDeviceFleetResult`	`createDeviceFleet(CreateDeviceFleetRequest request)` Creates a device fleet.
`CreateDomainResult`	`createDomain(CreateDomainRequest request)` Creates a `Domain` used by Amazon SageMaker Studio.
`CreateEdgePackagingJobResult`	`createEdgePackagingJob(CreateEdgePackagingJobRequest request)` Starts a SageMaker Edge Manager model packaging job.
`CreateEndpointResult`	`createEndpoint(CreateEndpointRequest request)` Creates an endpoint using the endpoint configuration specified in the request.
`CreateEndpointConfigResult`	`createEndpointConfig(CreateEndpointConfigRequest request)` Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
`CreateExperimentResult`	`createExperiment(CreateExperimentRequest request)` Creates an SageMaker experiment.
`CreateFeatureGroupResult`	`createFeatureGroup(CreateFeatureGroupRequest request)` Create a new `FeatureGroup`.
`CreateFlowDefinitionResult`	`createFlowDefinition(CreateFlowDefinitionRequest request)` Creates a flow definition.
`CreateHumanTaskUiResult`	`createHumanTaskUi(CreateHumanTaskUiRequest request)` Defines the settings you will use for the human review workflow user interface.
`CreateHyperParameterTuningJobResult`	`createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)` Starts a hyperparameter tuning job.
`CreateImageResult`	`createImage(CreateImageRequest request)` Creates a custom SageMaker image.
`CreateImageVersionResult`	`createImageVersion(CreateImageVersionRequest request)` Creates a version of the SageMaker image specified by `ImageName`.
`CreateInferenceRecommendationsJobResult`	`createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest request)` Starts a recommendation job.
`CreateLabelingJobResult`	`createLabelingJob(CreateLabelingJobRequest request)` Creates a job that uses workers to label the data objects in your input dataset.
`CreateModelResult`	`createModel(CreateModelRequest request)` Creates a model in Amazon SageMaker.
`CreateModelBiasJobDefinitionResult`	`createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request)` Creates the definition for a model bias job.
`CreateModelExplainabilityJobDefinitionResult`	`createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request)` Creates the definition for a model explainability job.
`CreateModelPackageResult`	`createModelPackage(CreateModelPackageRequest request)` Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.
`CreateModelPackageGroupResult`	`createModelPackageGroup(CreateModelPackageGroupRequest request)` Creates a model group.
`CreateModelQualityJobDefinitionResult`	`createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request)` Creates a definition for a job that monitors model quality and drift.
`CreateMonitoringScheduleResult`	`createMonitoringSchedule(CreateMonitoringScheduleRequest request)` Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
`CreateNotebookInstanceResult`	`createNotebookInstance(CreateNotebookInstanceRequest request)` Creates an Amazon SageMaker notebook instance.
`CreateNotebookInstanceLifecycleConfigResult`	`createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)` Creates a lifecycle configuration that you can associate with a notebook instance.
`CreatePipelineResult`	`createPipeline(CreatePipelineRequest request)` Creates a pipeline using a JSON pipeline definition.
`CreatePresignedDomainUrlResult`	`createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)` Creates a URL for a specified UserProfile in a Domain.
`CreatePresignedNotebookInstanceUrlResult`	`createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)` Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
`CreateProcessingJobResult`	`createProcessingJob(CreateProcessingJobRequest request)` Creates a processing job.
`CreateProjectResult`	`createProject(CreateProjectRequest request)` Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
`CreateStudioLifecycleConfigResult`	`createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest request)` Creates a new Studio Lifecycle Configuration.
`CreateTrainingJobResult`	`createTrainingJob(CreateTrainingJobRequest request)` Starts a model training job.
`CreateTransformJobResult`	`createTransformJob(CreateTransformJobRequest request)` Starts a transform job.
`CreateTrialResult`	`createTrial(CreateTrialRequest request)` Creates an SageMaker trial.
`CreateTrialComponentResult`	`createTrialComponent(CreateTrialComponentRequest request)` Creates a trial component, which is a stage of a machine learning trial.
`CreateUserProfileResult`	`createUserProfile(CreateUserProfileRequest request)` Creates a user profile.
`CreateWorkforceResult`	`createWorkforce(CreateWorkforceRequest request)` Use this operation to create a workforce.
`CreateWorkteamResult`	`createWorkteam(CreateWorkteamRequest request)` Creates a new work team for labeling your data.
`DeleteActionResult`	`deleteAction(DeleteActionRequest request)` Deletes an action.
`DeleteAlgorithmResult`	`deleteAlgorithm(DeleteAlgorithmRequest request)` Removes the specified algorithm from your account.
`DeleteAppResult`	`deleteApp(DeleteAppRequest request)` Used to stop and delete an app.
`DeleteAppImageConfigResult`	`deleteAppImageConfig(DeleteAppImageConfigRequest request)` Deletes an AppImageConfig.
`DeleteArtifactResult`	`deleteArtifact(DeleteArtifactRequest request)` Deletes an artifact.
`DeleteAssociationResult`	`deleteAssociation(DeleteAssociationRequest request)` Deletes an association.
`DeleteCodeRepositoryResult`	`deleteCodeRepository(DeleteCodeRepositoryRequest request)` Deletes the specified Git repository from your account.
`DeleteContextResult`	`deleteContext(DeleteContextRequest request)` Deletes an context.
`DeleteDataQualityJobDefinitionResult`	`deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request)` Deletes a data quality monitoring job definition.
`DeleteDeviceFleetResult`	`deleteDeviceFleet(DeleteDeviceFleetRequest request)` Deletes a fleet.
`DeleteDomainResult`	`deleteDomain(DeleteDomainRequest request)` Used to delete a domain.
`DeleteEndpointResult`	`deleteEndpoint(DeleteEndpointRequest request)` Deletes an endpoint.
`DeleteEndpointConfigResult`	`deleteEndpointConfig(DeleteEndpointConfigRequest request)` Deletes an endpoint configuration.
`DeleteExperimentResult`	`deleteExperiment(DeleteExperimentRequest request)` Deletes an SageMaker experiment.
`DeleteFeatureGroupResult`	`deleteFeatureGroup(DeleteFeatureGroupRequest request)` Delete the `FeatureGroup` and any data that was written to the `OnlineStore` of the `FeatureGroup`.
`DeleteFlowDefinitionResult`	`deleteFlowDefinition(DeleteFlowDefinitionRequest request)` Deletes the specified flow definition.
`DeleteHumanTaskUiResult`	`deleteHumanTaskUi(DeleteHumanTaskUiRequest request)` Use this operation to delete a human task user interface (worker task template).
`DeleteImageResult`	`deleteImage(DeleteImageRequest request)` Deletes a SageMaker image and all versions of the image.
`DeleteImageVersionResult`	`deleteImageVersion(DeleteImageVersionRequest request)` Deletes a version of a SageMaker image.
`DeleteModelResult`	`deleteModel(DeleteModelRequest request)` Deletes a model.
`DeleteModelBiasJobDefinitionResult`	`deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request)` Deletes an Amazon SageMaker model bias job definition.
`DeleteModelExplainabilityJobDefinitionResult`	`deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request)` Deletes an Amazon SageMaker model explainability job definition.
`DeleteModelPackageResult`	`deleteModelPackage(DeleteModelPackageRequest request)` Deletes a model package.
`DeleteModelPackageGroupResult`	`deleteModelPackageGroup(DeleteModelPackageGroupRequest request)` Deletes the specified model group.
`DeleteModelPackageGroupPolicyResult`	`deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request)` Deletes a model group resource policy.
`DeleteModelQualityJobDefinitionResult`	`deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request)` Deletes the secified model quality monitoring job definition.
`DeleteMonitoringScheduleResult`	`deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)` Deletes a monitoring schedule.
`DeleteNotebookInstanceResult`	`deleteNotebookInstance(DeleteNotebookInstanceRequest request)` Deletes an Amazon SageMaker notebook instance.
`DeleteNotebookInstanceLifecycleConfigResult`	`deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)` Deletes a notebook instance lifecycle configuration.
`DeletePipelineResult`	`deletePipeline(DeletePipelineRequest request)` Deletes a pipeline if there are no running instances of the pipeline.
`DeleteProjectResult`	`deleteProject(DeleteProjectRequest request)` Delete the specified project.
`DeleteStudioLifecycleConfigResult`	`deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest request)` Deletes the Studio Lifecycle Configuration.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest request)` Deletes the specified tags from an Amazon SageMaker resource.
`DeleteTrialResult`	`deleteTrial(DeleteTrialRequest request)` Deletes the specified trial.
`DeleteTrialComponentResult`	`deleteTrialComponent(DeleteTrialComponentRequest request)` Deletes the specified trial component.
`DeleteUserProfileResult`	`deleteUserProfile(DeleteUserProfileRequest request)` Deletes a user profile.
`DeleteWorkforceResult`	`deleteWorkforce(DeleteWorkforceRequest request)` Use this operation to delete a workforce.
`DeleteWorkteamResult`	`deleteWorkteam(DeleteWorkteamRequest request)` Deletes an existing work team.
`DeregisterDevicesResult`	`deregisterDevices(DeregisterDevicesRequest request)` Deregisters the specified devices.
`DescribeActionResult`	`describeAction(DescribeActionRequest request)` Describes an action.
`DescribeAlgorithmResult`	`describeAlgorithm(DescribeAlgorithmRequest request)` Returns a description of the specified algorithm that is in your account.
`DescribeAppResult`	`describeApp(DescribeAppRequest request)` Describes the app.
`DescribeAppImageConfigResult`	`describeAppImageConfig(DescribeAppImageConfigRequest request)` Describes an AppImageConfig.
`DescribeArtifactResult`	`describeArtifact(DescribeArtifactRequest request)` Describes an artifact.
`DescribeAutoMLJobResult`	`describeAutoMLJob(DescribeAutoMLJobRequest request)` Returns information about an Amazon SageMaker AutoML job.
`DescribeCodeRepositoryResult`	`describeCodeRepository(DescribeCodeRepositoryRequest request)` Gets details about the specified Git repository.
`DescribeCompilationJobResult`	`describeCompilationJob(DescribeCompilationJobRequest request)` Returns information about a model compilation job.
`DescribeContextResult`	`describeContext(DescribeContextRequest request)` Describes a context.
`DescribeDataQualityJobDefinitionResult`	`describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest request)` Gets the details of a data quality monitoring job definition.
`DescribeDeviceResult`	`describeDevice(DescribeDeviceRequest request)` Describes the device.
`DescribeDeviceFleetResult`	`describeDeviceFleet(DescribeDeviceFleetRequest request)` A description of the fleet the device belongs to.
`DescribeDomainResult`	`describeDomain(DescribeDomainRequest request)` The description of the domain.
`DescribeEdgePackagingJobResult`	`describeEdgePackagingJob(DescribeEdgePackagingJobRequest request)` A description of edge packaging jobs.
`DescribeEndpointResult`	`describeEndpoint(DescribeEndpointRequest request)` Returns the description of an endpoint.
`DescribeEndpointConfigResult`	`describeEndpointConfig(DescribeEndpointConfigRequest request)` Returns the description of an endpoint configuration created using the `CreateEndpointConfig` API.
`DescribeExperimentResult`	`describeExperiment(DescribeExperimentRequest request)` Provides a list of an experiment's properties.
`DescribeFeatureGroupResult`	`describeFeatureGroup(DescribeFeatureGroupRequest request)` Use this operation to describe a `FeatureGroup`.
`DescribeFlowDefinitionResult`	`describeFlowDefinition(DescribeFlowDefinitionRequest request)` Returns information about the specified flow definition.
`DescribeHumanTaskUiResult`	`describeHumanTaskUi(DescribeHumanTaskUiRequest request)` Returns information about the requested human task user interface (worker task template).
`DescribeHyperParameterTuningJobResult`	`describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)` Gets a description of a hyperparameter tuning job.
`DescribeImageResult`	`describeImage(DescribeImageRequest request)` Describes a SageMaker image.
`DescribeImageVersionResult`	`describeImageVersion(DescribeImageVersionRequest request)` Describes a version of a SageMaker image.
`DescribeInferenceRecommendationsJobResult`	`describeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest request)` Provides the results of the Inference Recommender job.
`DescribeLabelingJobResult`	`describeLabelingJob(DescribeLabelingJobRequest request)` Gets information about a labeling job.
`DescribeLineageGroupResult`	`describeLineageGroup(DescribeLineageGroupRequest request)` Provides a list of properties for the requested lineage group.
`DescribeModelResult`	`describeModel(DescribeModelRequest request)` Describes a model that you created using the `CreateModel` API.
`DescribeModelBiasJobDefinitionResult`	`describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest request)` Returns a description of a model bias job definition.
`DescribeModelExplainabilityJobDefinitionResult`	`describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest request)` Returns a description of a model explainability job definition.
`DescribeModelPackageResult`	`describeModelPackage(DescribeModelPackageRequest request)` Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
`DescribeModelPackageGroupResult`	`describeModelPackageGroup(DescribeModelPackageGroupRequest request)` Gets a description for the specified model group.
`DescribeModelQualityJobDefinitionResult`	`describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest request)` Returns a description of a model quality job definition.
`DescribeMonitoringScheduleResult`	`describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)` Describes the schedule for a monitoring job.
`DescribeNotebookInstanceResult`	`describeNotebookInstance(DescribeNotebookInstanceRequest request)` Returns information about a notebook instance.
`DescribeNotebookInstanceLifecycleConfigResult`	`describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)` Returns a description of a notebook instance lifecycle configuration.
`DescribePipelineResult`	`describePipeline(DescribePipelineRequest request)` Describes the details of a pipeline.
`DescribePipelineDefinitionForExecutionResult`	`describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest request)` Describes the details of an execution's pipeline definition.
`DescribePipelineExecutionResult`	`describePipelineExecution(DescribePipelineExecutionRequest request)` Describes the details of a pipeline execution.
`DescribeProcessingJobResult`	`describeProcessingJob(DescribeProcessingJobRequest request)` Returns a description of a processing job.
`DescribeProjectResult`	`describeProject(DescribeProjectRequest request)` Describes the details of a project.
`DescribeStudioLifecycleConfigResult`	`describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest request)` Describes the Studio Lifecycle Configuration.
`DescribeSubscribedWorkteamResult`	`describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)` Gets information about a work team provided by a vendor.
`DescribeTrainingJobResult`	`describeTrainingJob(DescribeTrainingJobRequest request)` Returns information about a training job.
`DescribeTransformJobResult`	`describeTransformJob(DescribeTransformJobRequest request)` Returns information about a transform job.
`DescribeTrialResult`	`describeTrial(DescribeTrialRequest request)` Provides a list of a trial's properties.
`DescribeTrialComponentResult`	`describeTrialComponent(DescribeTrialComponentRequest request)` Provides a list of a trials component's properties.
`DescribeUserProfileResult`	`describeUserProfile(DescribeUserProfileRequest request)` Describes a user profile.
`DescribeWorkforceResult`	`describeWorkforce(DescribeWorkforceRequest request)` Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
`DescribeWorkteamResult`	`describeWorkteam(DescribeWorkteamRequest request)` Gets information about a specific work team.
`DisableSagemakerServicecatalogPortfolioResult`	`disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest request)` Disables using Service Catalog in SageMaker.
`DisassociateTrialComponentResult`	`disassociateTrialComponent(DisassociateTrialComponentRequest request)` Disassociates a trial component from a trial.
`EnableSagemakerServicecatalogPortfolioResult`	`enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest request)` Enables using Service Catalog in SageMaker.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful, request, typically used for debugging issues where a service isn't acting as expected.
`GetDeviceFleetReportResult`	`getDeviceFleetReport(GetDeviceFleetReportRequest request)` Describes a fleet.
`GetLineageGroupPolicyResult`	`getLineageGroupPolicy(GetLineageGroupPolicyRequest request)` The resource policy for the lineage group.
`GetModelPackageGroupPolicyResult`	`getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest request)` Gets a resource policy that manages access for a model group.
`GetSagemakerServicecatalogPortfolioStatusResult`	`getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest request)` Gets the status of Service Catalog in SageMaker.
`GetSearchSuggestionsResult`	`getSearchSuggestions(GetSearchSuggestionsRequest request)` An auto-complete API for the search functionality in the Amazon SageMaker console.
`ListActionsResult`	`listActions(ListActionsRequest request)` Lists the actions in your account and their properties.
`ListAlgorithmsResult`	`listAlgorithms(ListAlgorithmsRequest request)` Lists the machine learning algorithms that have been created.
`ListAppImageConfigsResult`	`listAppImageConfigs(ListAppImageConfigsRequest request)` Lists the AppImageConfigs in your account and their properties.
`ListAppsResult`	`listApps(ListAppsRequest request)` Lists apps.
`ListArtifactsResult`	`listArtifacts(ListArtifactsRequest request)` Lists the artifacts in your account and their properties.
`ListAssociationsResult`	`listAssociations(ListAssociationsRequest request)` Lists the associations in your account and their properties.
`ListAutoMLJobsResult`	`listAutoMLJobs(ListAutoMLJobsRequest request)` Request a list of jobs.
`ListCandidatesForAutoMLJobResult`	`listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)` List the candidates created for the job.
`ListCodeRepositoriesResult`	`listCodeRepositories(ListCodeRepositoriesRequest request)` Gets a list of the Git repositories in your account.
`ListCompilationJobsResult`	`listCompilationJobs(ListCompilationJobsRequest request)` Lists model compilation jobs that satisfy various filters.
`ListContextsResult`	`listContexts(ListContextsRequest request)` Lists the contexts in your account and their properties.
`ListDataQualityJobDefinitionsResult`	`listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest request)` Lists the data quality job definitions in your account.
`ListDeviceFleetsResult`	`listDeviceFleets(ListDeviceFleetsRequest request)` Returns a list of devices in the fleet.
`ListDevicesResult`	`listDevices(ListDevicesRequest request)` A list of devices.
`ListDomainsResult`	`listDomains(ListDomainsRequest request)` Lists the domains.
`ListEdgePackagingJobsResult`	`listEdgePackagingJobs(ListEdgePackagingJobsRequest request)` Returns a list of edge packaging jobs.
`ListEndpointConfigsResult`	`listEndpointConfigs(ListEndpointConfigsRequest request)` Lists endpoint configurations.
`ListEndpointsResult`	`listEndpoints(ListEndpointsRequest request)` Lists endpoints.
`ListExperimentsResult`	`listExperiments(ListExperimentsRequest request)` Lists all the experiments in your account.
`ListFeatureGroupsResult`	`listFeatureGroups(ListFeatureGroupsRequest request)` List `FeatureGroup`s based on given filter and order.
`ListFlowDefinitionsResult`	`listFlowDefinitions(ListFlowDefinitionsRequest request)` Returns information about the flow definitions in your account.
`ListHumanTaskUisResult`	`listHumanTaskUis(ListHumanTaskUisRequest request)` Returns information about the human task user interfaces in your account.
`ListHyperParameterTuningJobsResult`	`listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)` Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
`ListImagesResult`	`listImages(ListImagesRequest request)` Lists the images in your account and their properties.
`ListImageVersionsResult`	`listImageVersions(ListImageVersionsRequest request)` Lists the versions of a specified image and their properties.
`ListInferenceRecommendationsJobsResult`	`listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest request)` Lists recommendation jobs that satisfy various filters.
`ListLabelingJobsResult`	`listLabelingJobs(ListLabelingJobsRequest request)` Gets a list of labeling jobs.
`ListLabelingJobsForWorkteamResult`	`listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)` Gets a list of labeling jobs assigned to a specified work team.
`ListLineageGroupsResult`	`listLineageGroups(ListLineageGroupsRequest request)` A list of lineage groups shared with your Amazon Web Services account.
`ListModelBiasJobDefinitionsResult`	`listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest request)` Lists model bias jobs definitions that satisfy various filters.
`ListModelExplainabilityJobDefinitionsResult`	`listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest request)` Lists model explainability job definitions that satisfy various filters.
`ListModelMetadataResult`	`listModelMetadata(ListModelMetadataRequest request)` Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
`ListModelPackageGroupsResult`	`listModelPackageGroups(ListModelPackageGroupsRequest request)` Gets a list of the model groups in your Amazon Web Services account.
`ListModelPackagesResult`	`listModelPackages(ListModelPackagesRequest request)` Lists the model packages that have been created.
`ListModelQualityJobDefinitionsResult`	`listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest request)` Gets a list of model quality monitoring job definitions in your account.
`ListModelsResult`	`listModels(ListModelsRequest request)` Lists models created with the `CreateModel` API.
`ListMonitoringExecutionsResult`	`listMonitoringExecutions(ListMonitoringExecutionsRequest request)` Returns list of all monitoring job executions.
`ListMonitoringSchedulesResult`	`listMonitoringSchedules(ListMonitoringSchedulesRequest request)` Returns list of all monitoring schedules.
`ListNotebookInstanceLifecycleConfigsResult`	`listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)` Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
`ListNotebookInstancesResult`	`listNotebookInstances(ListNotebookInstancesRequest request)` Returns a list of the Amazon SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
`ListPipelineExecutionsResult`	`listPipelineExecutions(ListPipelineExecutionsRequest request)` Gets a list of the pipeline executions.
`ListPipelineExecutionStepsResult`	`listPipelineExecutionSteps(ListPipelineExecutionStepsRequest request)` Gets a list of `PipeLineExecutionStep` objects.
`ListPipelineParametersForExecutionResult`	`listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest request)` Gets a list of parameters for a pipeline execution.
`ListPipelinesResult`	`listPipelines(ListPipelinesRequest request)` Gets a list of pipelines.
`ListProcessingJobsResult`	`listProcessingJobs(ListProcessingJobsRequest request)` Lists processing jobs that satisfy various filters.
`ListProjectsResult`	`listProjects(ListProjectsRequest request)` Gets a list of the projects in an Amazon Web Services account.
`ListStudioLifecycleConfigsResult`	`listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest request)` Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.
`ListSubscribedWorkteamsResult`	`listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)` Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
`ListTagsResult`	`listTags(ListTagsRequest request)` Returns the tags for the specified Amazon SageMaker resource.
`ListTrainingJobsResult`	`listTrainingJobs(ListTrainingJobsRequest request)` Lists training jobs.
`ListTrainingJobsForHyperParameterTuningJobResult`	`listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)` Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
`ListTransformJobsResult`	`listTransformJobs(ListTransformJobsRequest request)` Lists transform jobs.
`ListTrialComponentsResult`	`listTrialComponents(ListTrialComponentsRequest request)` Lists the trial components in your account.
`ListTrialsResult`	`listTrials(ListTrialsRequest request)` Lists the trials in your account.
`ListUserProfilesResult`	`listUserProfiles(ListUserProfilesRequest request)` Lists user profiles.
`ListWorkforcesResult`	`listWorkforces(ListWorkforcesRequest request)` Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
`ListWorkteamsResult`	`listWorkteams(ListWorkteamsRequest request)` Gets a list of private work teams that you have defined in a region.
`PutModelPackageGroupPolicyResult`	`putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest request)` Adds a resouce policy to control access to a model group.
`QueryLineageResult`	`queryLineage(QueryLineageRequest request)` Use this action to inspect your lineage and discover relationships between entities.
`RegisterDevicesResult`	`registerDevices(RegisterDevicesRequest request)` Register devices.
`RenderUiTemplateResult`	`renderUiTemplate(RenderUiTemplateRequest request)` Renders the UI template so that you can preview the worker's experience.
`RetryPipelineExecutionResult`	`retryPipelineExecution(RetryPipelineExecutionRequest request)` Retry the execution of the pipeline.
`SearchResult`	`search(SearchRequest request)` Finds Amazon SageMaker resources that match a search query.
`SendPipelineExecutionStepFailureResult`	`sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest request)` Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
`SendPipelineExecutionStepSuccessResult`	`sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest request)` Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`StartMonitoringScheduleResult`	`startMonitoringSchedule(StartMonitoringScheduleRequest request)` Starts a previously stopped monitoring schedule.
`StartNotebookInstanceResult`	`startNotebookInstance(StartNotebookInstanceRequest request)` Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
`StartPipelineExecutionResult`	`startPipelineExecution(StartPipelineExecutionRequest request)` Starts a pipeline execution.
`StopAutoMLJobResult`	`stopAutoMLJob(StopAutoMLJobRequest request)` A method for forcing the termination of a running job.
`StopCompilationJobResult`	`stopCompilationJob(StopCompilationJobRequest request)` Stops a model compilation job.
`StopEdgePackagingJobResult`	`stopEdgePackagingJob(StopEdgePackagingJobRequest request)` Request to stop an edge packaging job.
`StopHyperParameterTuningJobResult`	`stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)` Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
`StopInferenceRecommendationsJobResult`	`stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest request)` Stops an Inference Recommender job.
`StopLabelingJobResult`	`stopLabelingJob(StopLabelingJobRequest request)` Stops a running labeling job.
`StopMonitoringScheduleResult`	`stopMonitoringSchedule(StopMonitoringScheduleRequest request)` Stops a previously started monitoring schedule.
`StopNotebookInstanceResult`	`stopNotebookInstance(StopNotebookInstanceRequest request)` Terminates the ML compute instance.
`StopPipelineExecutionResult`	`stopPipelineExecution(StopPipelineExecutionRequest request)` Stops a pipeline execution.
`StopProcessingJobResult`	`stopProcessingJob(StopProcessingJobRequest request)` Stops a processing job.
`StopTrainingJobResult`	`stopTrainingJob(StopTrainingJobRequest request)` Stops a training job.
`StopTransformJobResult`	`stopTransformJob(StopTransformJobRequest request)` Stops a batch transform job.
`UpdateActionResult`	`updateAction(UpdateActionRequest request)` Updates an action.
`UpdateAppImageConfigResult`	`updateAppImageConfig(UpdateAppImageConfigRequest request)` Updates the properties of an AppImageConfig.
`UpdateArtifactResult`	`updateArtifact(UpdateArtifactRequest request)` Updates an artifact.
`UpdateCodeRepositoryResult`	`updateCodeRepository(UpdateCodeRepositoryRequest request)` Updates the specified Git repository with the specified values.
`UpdateContextResult`	`updateContext(UpdateContextRequest request)` Updates a context.
`UpdateDeviceFleetResult`	`updateDeviceFleet(UpdateDeviceFleetRequest request)` Updates a fleet of devices.
`UpdateDevicesResult`	`updateDevices(UpdateDevicesRequest request)` Updates one or more devices in a fleet.
`UpdateDomainResult`	`updateDomain(UpdateDomainRequest request)` Updates the default settings for new user profiles in the domain.
`UpdateEndpointResult`	`updateEndpoint(UpdateEndpointRequest request)` Deploys the new `EndpointConfig` specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous `EndpointConfig` (there is no availability loss).
`UpdateEndpointWeightsAndCapacitiesResult`	`updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)` Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
`UpdateExperimentResult`	`updateExperiment(UpdateExperimentRequest request)` Adds, updates, or removes the description of an experiment.
`UpdateImageResult`	`updateImage(UpdateImageRequest request)` Updates the properties of a SageMaker image.
`UpdateModelPackageResult`	`updateModelPackage(UpdateModelPackageRequest request)` Updates a versioned model.
`UpdateMonitoringScheduleResult`	`updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)` Updates a previously created schedule.
`UpdateNotebookInstanceResult`	`updateNotebookInstance(UpdateNotebookInstanceRequest request)` Updates a notebook instance.
`UpdateNotebookInstanceLifecycleConfigResult`	`updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)` Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
`UpdatePipelineResult`	`updatePipeline(UpdatePipelineRequest request)` Updates a pipeline.
`UpdatePipelineExecutionResult`	`updatePipelineExecution(UpdatePipelineExecutionRequest request)` Updates a pipeline execution.
`UpdateProjectResult`	`updateProject(UpdateProjectRequest request)` Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
`UpdateTrainingJobResult`	`updateTrainingJob(UpdateTrainingJobRequest request)` Update a model training job to request a new Debugger profiling configuration.
`UpdateTrialResult`	`updateTrial(UpdateTrialRequest request)` Updates the display name of a trial.
`UpdateTrialComponentResult`	`updateTrialComponent(UpdateTrialComponentRequest request)` Updates one or more properties of a trial component.
`UpdateUserProfileResult`	`updateUserProfile(UpdateUserProfileRequest request)` Updates a user profile.
`UpdateWorkforceResult`	`updateWorkforce(UpdateWorkforceRequest request)` Use this operation to update your workforce.
`UpdateWorkteamResult`	`updateWorkteam(UpdateWorkteamRequest request)` Updates an existing work team with new member definitions or description.
`AmazonSageMakerWaiters`	`waiters()`

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - builder
```
public static AmazonSageMakerClientBuilder builder()
```
  - addAssociation
```
public AddAssociationResult addAssociation(AddAssociationRequest request)
```
    Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Specified by:
    
    addAssociation in interface AmazonSageMaker
    
    Parameters:
    
    addAssociationRequest -
    
    Returns:
    
    Result of the AddAssociation operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - addTags
```
public AddTagsResult addTags(AddTagsRequest request)
```
    Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
    
    Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
    
    Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
    
    Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.
    
    Specified by:
    
    addTags in interface AmazonSageMaker
    
    Parameters:
    
    addTagsRequest -
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - associateTrialComponent
```
public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request)
```
    Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
    
    Specified by:
    
    associateTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    associateTrialComponentRequest -
    
    Returns:
    
    Result of the AssociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - batchDescribeModelPackage
```
public BatchDescribeModelPackageResult batchDescribeModelPackage(BatchDescribeModelPackageRequest request)
```
    This action batch describes a list of versioned model packages
    
    Specified by:
    
    batchDescribeModelPackage in interface AmazonSageMaker
    
    Parameters:
    
    batchDescribeModelPackageRequest -
    
    Returns:
    
    Result of the BatchDescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createAction
```
public CreateActionResult createAction(CreateActionRequest request)
```
    Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Specified by:
    
    createAction in interface AmazonSageMaker
    
    Parameters:
    
    createActionRequest -
    
    Returns:
    
    Result of the CreateAction operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAlgorithm
```
public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request)
```
    Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
    
    Specified by:
    
    createAlgorithm in interface AmazonSageMaker
    
    Parameters:
    
    createAlgorithmRequest -
    
    Returns:
    
    Result of the CreateAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createApp
```
public CreateAppResult createApp(CreateAppRequest request)
```
    Creates a running app for the specified UserProfile. Supported apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
    
    Specified by:
    
    createApp in interface AmazonSageMaker
    
    Parameters:
    
    createAppRequest -
    
    Returns:
    
    Result of the CreateApp operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createAppImageConfig
```
public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request)
```
    Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
    
    Specified by:
    
    createAppImageConfig in interface AmazonSageMaker
    
    Parameters:
    
    createAppImageConfigRequest -
    
    Returns:
    
    Result of the CreateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createArtifact
```
public CreateArtifactResult createArtifact(CreateArtifactRequest request)
```
    Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Specified by:
    
    createArtifact in interface AmazonSageMaker
    
    Parameters:
    
    createArtifactRequest -
    
    Returns:
    
    Result of the CreateArtifact operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAutoMLJob
```
public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request)
```
    Creates an Autopilot job.
    
    Find the best-performing model after you run an Autopilot job by calling .
    
    For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
    
    Specified by:
    
    createAutoMLJob in interface AmazonSageMaker
    
    Parameters:
    
    createAutoMLJobRequest -
    
    Returns:
    
    Result of the CreateAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createCodeRepository
```
public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request)
```
    Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
    
    The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
    
    Specified by:
    
    createCodeRepository in interface AmazonSageMaker
    
    Parameters:
    
    createCodeRepositoryRequest -
    
    Returns:
    
    Result of the CreateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createCompilationJob
```
public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request)
```
    Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
    
    If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
    
    In the request body, you provide the following:
    - A name for the compilation job
    - Information about the input model artifacts
    - The output location for the compiled model and the device (target) that the model runs on
    - The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
    
    To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
    Specified by:
    
    createCompilationJob in interface AmazonSageMaker
    
    Parameters:
    
    createCompilationJobRequest -
    
    Returns:
    
    Result of the CreateCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createContext
```
public CreateContextResult createContext(CreateContextRequest request)
```
    Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Specified by:
    
    createContext in interface AmazonSageMaker
    
    Parameters:
    
    createContextRequest -
    
    Returns:
    
    Result of the CreateContext operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createDataQualityJobDefinition
```
public CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request)
```
    Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
    
    Specified by:
    
    createDataQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    createDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createDeviceFleet
```
public CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request)
```
    Creates a device fleet.
    
    Specified by:
    
    createDeviceFleet in interface AmazonSageMaker
    
    Parameters:
    
    createDeviceFleetRequest -
    
    Returns:
    
    Result of the CreateDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createDomain
```
public CreateDomainResult createDomain(CreateDomainRequest request)
```
    Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
    
    EFS storage
    
    When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
    
    SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
    
    VPC configuration
    
    All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:
    - PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.
    - VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.
      
      When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.
    
    For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
    Specified by:
    
    createDomain in interface AmazonSageMaker
    
    Parameters:
    
    createDomainRequest -
    
    Returns:
    
    Result of the CreateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createEdgePackagingJob
```
public CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request)
```
    Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
    
    Specified by:
    
    createEdgePackagingJob in interface AmazonSageMaker
    
    Parameters:
    
    createEdgePackagingJobRequest -
    
    Returns:
    
    Result of the CreateEdgePackagingJob operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEndpoint
```
public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
```
    Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
    
    Use this API to deploy models using Amazon SageMaker hosting services.
    
    For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see the Create Endpoint example notebook.
    
    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
    
    The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
    
    When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
    
    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
    
    When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
    
    If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
    
    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
    
    Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
    
    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
    
    "Resource": [
    
    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
    
    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
    
    ]
    
    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
    Specified by:
    
    createEndpoint in interface AmazonSageMaker
    
    Parameters:
    
    createEndpointRequest -
    
    Returns:
    
    Result of the CreateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEndpointConfig
```
public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
```
    Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
    
    Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
    
    In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.
    
    If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
    
    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
    
    Specified by:
    
    createEndpointConfig in interface AmazonSageMaker
    
    Parameters:
    
    createEndpointConfigRequest -
    
    Returns:
    
    Result of the CreateEndpointConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createExperiment
```
public CreateExperimentResult createExperiment(CreateExperimentRequest request)
```
    Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
    
    The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
    
    To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.
    
    To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
    
    Specified by:
    
    createExperiment in interface AmazonSageMaker
    
    Parameters:
    
    createExperimentRequest -
    
    Returns:
    
    Result of the CreateExperiment operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createFeatureGroup
```
public CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest request)
```
    Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.
    
    The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.
    
    You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.
    
    Specified by:
    
    createFeatureGroup in interface AmazonSageMaker
    
    Parameters:
    
    createFeatureGroupRequest -
    
    Returns:
    
    Result of the CreateFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createFlowDefinition
```
public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request)
```
    Creates a flow definition.
    
    Specified by:
    
    createFlowDefinition in interface AmazonSageMaker
    
    Parameters:
    
    createFlowDefinitionRequest -
    
    Returns:
    
    Result of the CreateFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHumanTaskUi
```
public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request)
```
    Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
    
    Specified by:
    
    createHumanTaskUi in interface AmazonSageMaker
    
    Parameters:
    
    createHumanTaskUiRequest -
    
    Returns:
    
    Result of the CreateHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHyperParameterTuningJob
```
public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
```
    Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
    
    Specified by:
    
    createHyperParameterTuningJob in interface AmazonSageMaker
    
    Parameters:
    
    createHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the CreateHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImage
```
public CreateImageResult createImage(CreateImageRequest request)
```
    Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
    
    Specified by:
    
    createImage in interface AmazonSageMaker
    
    Parameters:
    
    createImageRequest -
    
    Returns:
    
    Result of the CreateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImageVersion
```
public CreateImageVersionResult createImageVersion(CreateImageVersionRequest request)
```
    Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.
    
    Specified by:
    
    createImageVersion in interface AmazonSageMaker
    
    Parameters:
    
    createImageVersionRequest -
    
    Returns:
    
    Result of the CreateImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createInferenceRecommendationsJob
```
public CreateInferenceRecommendationsJobResult createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest request)
```
    Starts a recommendation job. You can create either an instance recommendation or load test job.
    
    Specified by:
    
    createInferenceRecommendationsJob in interface AmazonSageMaker
    
    Parameters:
    
    createInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the CreateInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createLabelingJob
```
public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request)
```
    Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
    
    You can select your workforce from one of three providers:
    - A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
    - One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
    - The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
    You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
    
    The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
    
    The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
    
    You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
    Specified by:
    
    createLabelingJob in interface AmazonSageMaker
    
    Parameters:
    
    createLabelingJobRequest -
    
    Returns:
    
    Result of the CreateLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModel
```
public CreateModelResult createModel(CreateModelRequest request)
```
    Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
    
    Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
    
    To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
    
    For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).
    
    To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
    
    In the CreateModel request, you must define a container with the PrimaryContainer parameter.
    
    In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
    
    Specified by:
    
    createModel in interface AmazonSageMaker
    
    Parameters:
    
    createModelRequest -
    
    Returns:
    
    Result of the CreateModel operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelBiasJobDefinition
```
public CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request)
```
    Creates the definition for a model bias job.
    
    Specified by:
    
    createModelBiasJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    createModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createModelExplainabilityJobDefinition
```
public CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request)
```
    Creates the definition for a model explainability job.
    
    Specified by:
    
    createModelExplainabilityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    createModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createModelPackage
```
public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request)
```
    Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
    
    To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.
    There are two types of model packages:
    
    Versioned - a model that is part of a model group in the model registry.
    
    Unversioned - a model package that is not part of a model group.
    Specified by:
    
    createModelPackage in interface AmazonSageMaker
    
    Parameters:
    
    createModelPackageRequest -
    
    Returns:
    
    Result of the CreateModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelPackageGroup
```
public CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request)
```
    Creates a model group. A model group contains a group of model versions.
    
    Specified by:
    
    createModelPackageGroup in interface AmazonSageMaker
    
    Parameters:
    
    createModelPackageGroupRequest -
    
    Returns:
    
    Result of the CreateModelPackageGroup operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelQualityJobDefinition
```
public CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request)
```
    Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
    
    Specified by:
    
    createModelQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    createModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createMonitoringSchedule
```
public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request)
```
    Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
    
    Specified by:
    
    createMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    createMonitoringScheduleRequest -
    
    Returns:
    
    Result of the CreateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstance
```
public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
```
    Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
    
    In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
    
    Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
    
    After receiving the request, Amazon SageMaker does the following:
    1. Creates a network interface in the Amazon SageMaker VPC.
    2. (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.
    3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
    After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
    
    After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
    
    For more information, see How It Works.
    Specified by:
    
    createNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    createNotebookInstanceRequest -
    
    Returns:
    
    Result of the CreateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstanceLifecycleConfig
```
public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
```
    Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
    
    Each lifecycle configuration script has a limit of 16384 characters.
    
    The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
    
    View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
    
    Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
    
    For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
    
    Specified by:
    
    createNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    createNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createPipeline
```
public CreatePipelineResult createPipeline(CreatePipelineRequest request)
```
    Creates a pipeline using a JSON pipeline definition.
    
    Specified by:
    
    createPipeline in interface AmazonSageMaker
    
    Parameters:
    
    createPipelineRequest -
    
    Returns:
    
    Result of the CreatePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createPresignedDomainUrl
```
public CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)
```
    Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
    
    The IAM role or user used to call this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
    
    You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .
    
    The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
    
    Specified by:
    
    createPresignedDomainUrl in interface AmazonSageMaker
    
    Parameters:
    
    createPresignedDomainUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedDomainUrl operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createPresignedNotebookInstanceUrl
```
public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
```
    Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
    
    The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
    
    You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
    
    The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
    
    Specified by:
    
    createPresignedNotebookInstanceUrl in interface AmazonSageMaker
    
    Parameters:
    
    createPresignedNotebookInstanceUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createProcessingJob
```
public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request)
```
    Creates a processing job.
    
    Specified by:
    
    createProcessingJob in interface AmazonSageMaker
    
    Parameters:
    
    createProcessingJobRequest -
    
    Returns:
    
    Result of the CreateProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createProject
```
public CreateProjectResult createProject(CreateProjectRequest request)
```
    Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
    
    Specified by:
    
    createProject in interface AmazonSageMaker
    
    Parameters:
    
    createProjectRequest -
    
    Returns:
    
    Result of the CreateProject operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createStudioLifecycleConfig
```
public CreateStudioLifecycleConfigResult createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest request)
```
    Creates a new Studio Lifecycle Configuration.
    
    Specified by:
    
    createStudioLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    createStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the CreateStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createTrainingJob
```
public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
```
    Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
    
    If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inference.
    
    In the request body, you provide the following:
    - AlgorithmSpecification - Identifies the training algorithm to use.
    - HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
    - InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
    - OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of model training.
    - ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
    - EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.
    - RoleArn - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
    - StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
    - Environment - The environment variables to set in the Docker container.
    - RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.
    For more information about Amazon SageMaker, see How It Works.
    Specified by:
    
    createTrainingJob in interface AmazonSageMaker
    
    Parameters:
    
    createTrainingJobRequest -
    
    Returns:
    
    Result of the CreateTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTransformJob
```
public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
```
    Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
    
    To perform batch transformations, you create a transform job and use the data that you have readily available.
    
    In the request body, you provide the following:
    - TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
    - ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.
    - TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
    - TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
    - TransformResources - Identifies the ML compute instances for the transform job.
    For more information about how batch transformation works, see Batch Transform.
    Specified by:
    
    createTransformJob in interface AmazonSageMaker
    
    Parameters:
    
    createTransformJobRequest -
    
    Returns:
    
    Result of the CreateTransformJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTrial
```
public CreateTrialResult createTrial(CreateTrialRequest request)
```
    Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to a trial and then use the Search API to search for the tags.
    
    To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
    
    Specified by:
    
    createTrial in interface AmazonSageMaker
    
    Parameters:
    
    createTrialRequest -
    
    Returns:
    
    Result of the CreateTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createTrialComponent
```
public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request)
```
    Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
    
    Trial components include pre-processing jobs, training jobs, and batch transform jobs.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to a trial component and then use the Search API to search for the tags.
    
    Specified by:
    
    createTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    createTrialComponentRequest -
    
    Returns:
    
    Result of the CreateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createUserProfile
```
public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request)
```
    Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
    
    Specified by:
    
    createUserProfile in interface AmazonSageMaker
    
    Parameters:
    
    createUserProfileRequest -
    
    Returns:
    
    Result of the CreateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createWorkforce
```
public CreateWorkforceResult createWorkforce(CreateWorkforceRequest request)
```
    Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
    
    If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
    
    To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).
    
    To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
    
    Specified by:
    
    createWorkforce in interface AmazonSageMaker
    
    Parameters:
    
    createWorkforceRequest -
    
    Returns:
    
    Result of the CreateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createWorkteam
```
public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request)
```
    Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
    
    You cannot create more than 25 work teams in an account and region.
    
    Specified by:
    
    createWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    createWorkteamRequest -
    
    Returns:
    
    Result of the CreateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - deleteAction
```
public DeleteActionResult deleteAction(DeleteActionRequest request)
```
    Deletes an action.
    
    Specified by:
    
    deleteAction in interface AmazonSageMaker
    
    Parameters:
    
    deleteActionRequest -
    
    Returns:
    
    Result of the DeleteAction operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAlgorithm
```
public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request)
```
    Removes the specified algorithm from your account.
    
    Specified by:
    
    deleteAlgorithm in interface AmazonSageMaker
    
    Parameters:
    
    deleteAlgorithmRequest -
    
    Returns:
    
    Result of the DeleteAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteApp
```
public DeleteAppResult deleteApp(DeleteAppRequest request)
```
    Used to stop and delete an app.
    
    Specified by:
    
    deleteApp in interface AmazonSageMaker
    
    Parameters:
    
    deleteAppRequest -
    
    Returns:
    
    Result of the DeleteApp operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAppImageConfig
```
public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request)
```
    Deletes an AppImageConfig.
    
    Specified by:
    
    deleteAppImageConfig in interface AmazonSageMaker
    
    Parameters:
    
    deleteAppImageConfigRequest -
    
    Returns:
    
    Result of the DeleteAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteArtifact
```
public DeleteArtifactResult deleteArtifact(DeleteArtifactRequest request)
```
    Deletes an artifact. Either ArtifactArn or Source must be specified.
    
    Specified by:
    
    deleteArtifact in interface AmazonSageMaker
    
    Parameters:
    
    deleteArtifactRequest -
    
    Returns:
    
    Result of the DeleteArtifact operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAssociation
```
public DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request)
```
    Deletes an association.
    
    Specified by:
    
    deleteAssociation in interface AmazonSageMaker
    
    Parameters:
    
    deleteAssociationRequest -
    
    Returns:
    
    Result of the DeleteAssociation operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteCodeRepository
```
public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request)
```
    Deletes the specified Git repository from your account.
    
    Specified by:
    
    deleteCodeRepository in interface AmazonSageMaker
    
    Parameters:
    
    deleteCodeRepositoryRequest -
    
    Returns:
    
    Result of the DeleteCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteContext
```
public DeleteContextResult deleteContext(DeleteContextRequest request)
```
    Deletes an context.
    
    Specified by:
    
    deleteContext in interface AmazonSageMaker
    
    Parameters:
    
    deleteContextRequest -
    
    Returns:
    
    Result of the DeleteContext operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteDataQualityJobDefinition
```
public DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request)
```
    Deletes a data quality monitoring job definition.
    
    Specified by:
    
    deleteDataQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    deleteDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteDeviceFleet
```
public DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request)
```
    Deletes a fleet.
    
    Specified by:
    
    deleteDeviceFleet in interface AmazonSageMaker
    
    Parameters:
    
    deleteDeviceFleetRequest -
    
    Returns:
    
    Result of the DeleteDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteDomain
```
public DeleteDomainResult deleteDomain(DeleteDomainRequest request)
```
    Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
    
    Specified by:
    
    deleteDomain in interface AmazonSageMaker
    
    Parameters:
    
    deleteDomainRequest -
    
    Returns:
    
    Result of the DeleteDomain operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpoint
```
public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
```
    Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
    
    Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
    
    Specified by:
    
    deleteEndpoint in interface AmazonSageMaker
    
    Parameters:
    
    deleteEndpointRequest -
    
    Returns:
    
    Result of the DeleteEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpointConfig
```
public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
```
    Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
    
    You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
    
    Specified by:
    
    deleteEndpointConfig in interface AmazonSageMaker
    
    Parameters:
    
    deleteEndpointConfigRequest -
    
    Returns:
    
    Result of the DeleteEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteExperiment
```
public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request)
```
    Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
    
    Specified by:
    
    deleteExperiment in interface AmazonSageMaker
    
    Parameters:
    
    deleteExperimentRequest -
    
    Returns:
    
    Result of the DeleteExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteFeatureGroup
```
public DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest request)
```
    Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
    
    Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.
    
    Specified by:
    
    deleteFeatureGroup in interface AmazonSageMaker
    
    Parameters:
    
    deleteFeatureGroupRequest -
    
    Returns:
    
    Result of the DeleteFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteFlowDefinition
```
public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request)
```
    Deletes the specified flow definition.
    
    Specified by:
    
    deleteFlowDefinition in interface AmazonSageMaker
    
    Parameters:
    
    deleteFlowDefinitionRequest -
    
    Returns:
    
    Result of the DeleteFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHumanTaskUi
```
public DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request)
```
    Use this operation to delete a human task user interface (worker task template).
    
    To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
    
    Specified by:
    
    deleteHumanTaskUi in interface AmazonSageMaker
    
    Parameters:
    
    deleteHumanTaskUiRequest -
    
    Returns:
    
    Result of the DeleteHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteImage
```
public DeleteImageResult deleteImage(DeleteImageRequest request)
```
    Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
    
    Specified by:
    
    deleteImage in interface AmazonSageMaker
    
    Parameters:
    
    deleteImageRequest -
    
    Returns:
    
    Result of the DeleteImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteImageVersion
```
public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request)
```
    Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
    
    Specified by:
    
    deleteImageVersion in interface AmazonSageMaker
    
    Parameters:
    
    deleteImageVersionRequest -
    
    Returns:
    
    Result of the DeleteImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModel
```
public DeleteModelResult deleteModel(DeleteModelRequest request)
```
    Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
    
    Specified by:
    
    deleteModel in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelRequest -
    
    Returns:
    
    Result of the DeleteModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelBiasJobDefinition
```
public DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request)
```
    Deletes an Amazon SageMaker model bias job definition.
    
    Specified by:
    
    deleteModelBiasJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModelExplainabilityJobDefinition
```
public DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request)
```
    Deletes an Amazon SageMaker model explainability job definition.
    
    Specified by:
    
    deleteModelExplainabilityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackage
```
public DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request)
```
    Deletes a model package.
    
    A model package is used to create Amazon SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
    
    Specified by:
    
    deleteModelPackage in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelPackageRequest -
    
    Returns:
    
    Result of the DeleteModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackageGroup
```
public DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest request)
```
    Deletes the specified model group.
    
    Specified by:
    
    deleteModelPackageGroup in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelPackageGroupRequest -
    
    Returns:
    
    Result of the DeleteModelPackageGroup operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackageGroupPolicy
```
public DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request)
```
    Deletes a model group resource policy.
    
    Specified by:
    
    deleteModelPackageGroupPolicy in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the DeleteModelPackageGroupPolicy operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelQualityJobDefinition
```
public DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request)
```
    Deletes the secified model quality monitoring job definition.
    
    Specified by:
    
    deleteModelQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    deleteModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteMonitoringSchedule
```
public DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)
```
    Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
    
    Specified by:
    
    deleteMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    deleteMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DeleteMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstance
```
public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
```
    Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.
    
    When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
    
    Specified by:
    
    deleteNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    deleteNotebookInstanceRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstanceLifecycleConfig
```
public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
```
    Deletes a notebook instance lifecycle configuration.
    
    Specified by:
    
    deleteNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    deleteNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deletePipeline
```
public DeletePipelineResult deletePipeline(DeletePipelineRequest request)
```
    Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
    
    Specified by:
    
    deletePipeline in interface AmazonSageMaker
    
    Parameters:
    
    deletePipelineRequest -
    
    Returns:
    
    Result of the DeletePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteProject
```
public DeleteProjectResult deleteProject(DeleteProjectRequest request)
```
    Delete the specified project.
    
    Specified by:
    
    deleteProject in interface AmazonSageMaker
    
    Parameters:
    
    deleteProjectRequest -
    
    Returns:
    
    Result of the DeleteProject operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteStudioLifecycleConfig
```
public DeleteStudioLifecycleConfigResult deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest request)
```
    Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
    
    Specified by:
    
    deleteStudioLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    deleteStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the DeleteStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteTags
```
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
```
    Deletes the specified tags from an Amazon SageMaker resource.
    
    To list a resource's tags, use the ListTags API.
    
    When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
    
    When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.
    
    Specified by:
    
    deleteTags in interface AmazonSageMaker
    
    Parameters:
    
    deleteTagsRequest -
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTrial
```
public DeleteTrialResult deleteTrial(DeleteTrialRequest request)
```
    Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
    
    Specified by:
    
    deleteTrial in interface AmazonSageMaker
    
    Parameters:
    
    deleteTrialRequest -
    
    Returns:
    
    Result of the DeleteTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteTrialComponent
```
public DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request)
```
    Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
    
    Specified by:
    
    deleteTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    deleteTrialComponentRequest -
    
    Returns:
    
    Result of the DeleteTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteUserProfile
```
public DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request)
```
    Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
    
    Specified by:
    
    deleteUserProfile in interface AmazonSageMaker
    
    Parameters:
    
    deleteUserProfileRequest -
    
    Returns:
    
    Result of the DeleteUserProfile operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkforce
```
public DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request)
```
    Use this operation to delete a workforce.
    
    If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
    
    If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.
    
    Specified by:
    
    deleteWorkforce in interface AmazonSageMaker
    
    Parameters:
    
    deleteWorkforceRequest -
    
    Returns:
    
    Result of the DeleteWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkteam
```
public DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest request)
```
    Deletes an existing work team. This operation can't be undone.
    
    Specified by:
    
    deleteWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    deleteWorkteamRequest -
    
    Returns:
    
    Result of the DeleteWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - deregisterDevices
```
public DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest request)
```
    Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
    
    Specified by:
    
    deregisterDevices in interface AmazonSageMaker
    
    Parameters:
    
    deregisterDevicesRequest -
    
    Returns:
    
    Result of the DeregisterDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeAction
```
public DescribeActionResult describeAction(DescribeActionRequest request)
```
    Describes an action.
    
    Specified by:
    
    describeAction in interface AmazonSageMaker
    
    Parameters:
    
    describeActionRequest -
    
    Returns:
    
    Result of the DescribeAction operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAlgorithm
```
public DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest request)
```
    Returns a description of the specified algorithm that is in your account.
    
    Specified by:
    
    describeAlgorithm in interface AmazonSageMaker
    
    Parameters:
    
    describeAlgorithmRequest -
    
    Returns:
    
    Result of the DescribeAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeApp
```
public DescribeAppResult describeApp(DescribeAppRequest request)
```
    Describes the app.
    
    Specified by:
    
    describeApp in interface AmazonSageMaker
    
    Parameters:
    
    describeAppRequest -
    
    Returns:
    
    Result of the DescribeApp operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAppImageConfig
```
public DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest request)
```
    Describes an AppImageConfig.
    
    Specified by:
    
    describeAppImageConfig in interface AmazonSageMaker
    
    Parameters:
    
    describeAppImageConfigRequest -
    
    Returns:
    
    Result of the DescribeAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeArtifact
```
public DescribeArtifactResult describeArtifact(DescribeArtifactRequest request)
```
    Describes an artifact.
    
    Specified by:
    
    describeArtifact in interface AmazonSageMaker
    
    Parameters:
    
    describeArtifactRequest -
    
    Returns:
    
    Result of the DescribeArtifact operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAutoMLJob
```
public DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest request)
```
    Returns information about an Amazon SageMaker AutoML job.
    
    Specified by:
    
    describeAutoMLJob in interface AmazonSageMaker
    
    Parameters:
    
    describeAutoMLJobRequest -
    
    Returns:
    
    Result of the DescribeAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeCodeRepository
```
public DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest request)
```
    Gets details about the specified Git repository.
    
    Specified by:
    
    describeCodeRepository in interface AmazonSageMaker
    
    Parameters:
    
    describeCodeRepositoryRequest -
    
    Returns:
    
    Result of the DescribeCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeCompilationJob
```
public DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest request)
```
    Returns information about a model compilation job.
    
    To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
    
    Specified by:
    
    describeCompilationJob in interface AmazonSageMaker
    
    Parameters:
    
    describeCompilationJobRequest -
    
    Returns:
    
    Result of the DescribeCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeContext
```
public DescribeContextResult describeContext(DescribeContextRequest request)
```
    Describes a context.
    
    Specified by:
    
    describeContext in interface AmazonSageMaker
    
    Parameters:
    
    describeContextRequest -
    
    Returns:
    
    Result of the DescribeContext operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDataQualityJobDefinition
```
public DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest request)
```
    Gets the details of a data quality monitoring job definition.
    
    Specified by:
    
    describeDataQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    describeDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDevice
```
public DescribeDeviceResult describeDevice(DescribeDeviceRequest request)
```
    Describes the device.
    
    Specified by:
    
    describeDevice in interface AmazonSageMaker
    
    Parameters:
    
    describeDeviceRequest -
    
    Returns:
    
    Result of the DescribeDevice operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDeviceFleet
```
public DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest request)
```
    A description of the fleet the device belongs to.
    
    Specified by:
    
    describeDeviceFleet in interface AmazonSageMaker
    
    Parameters:
    
    describeDeviceFleetRequest -
    
    Returns:
    
    Result of the DescribeDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDomain
```
public DescribeDomainResult describeDomain(DescribeDomainRequest request)
```
    The description of the domain.
    
    Specified by:
    
    describeDomain in interface AmazonSageMaker
    
    Parameters:
    
    describeDomainRequest -
    
    Returns:
    
    Result of the DescribeDomain operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEdgePackagingJob
```
public DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest request)
```
    A description of edge packaging jobs.
    
    Specified by:
    
    describeEdgePackagingJob in interface AmazonSageMaker
    
    Parameters:
    
    describeEdgePackagingJobRequest -
    
    Returns:
    
    Result of the DescribeEdgePackagingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEndpoint
```
public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
```
    Returns the description of an endpoint.
    
    Specified by:
    
    describeEndpoint in interface AmazonSageMaker
    
    Parameters:
    
    describeEndpointRequest -
    
    Returns:
    
    Result of the DescribeEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpointConfig
```
public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
```
    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
    
    Specified by:
    
    describeEndpointConfig in interface AmazonSageMaker
    
    Parameters:
    
    describeEndpointConfigRequest -
    
    Returns:
    
    Result of the DescribeEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeExperiment
```
public DescribeExperimentResult describeExperiment(DescribeExperimentRequest request)
```
    Provides a list of an experiment's properties.
    
    Specified by:
    
    describeExperiment in interface AmazonSageMaker
    
    Parameters:
    
    describeExperimentRequest -
    
    Returns:
    
    Result of the DescribeExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFeatureGroup
```
public DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest request)
```
    Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
    
    Specified by:
    
    describeFeatureGroup in interface AmazonSageMaker
    
    Parameters:
    
    describeFeatureGroupRequest -
    
    Returns:
    
    Result of the DescribeFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFlowDefinition
```
public DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest request)
```
    Returns information about the specified flow definition.
    
    Specified by:
    
    describeFlowDefinition in interface AmazonSageMaker
    
    Parameters:
    
    describeFlowDefinitionRequest -
    
    Returns:
    
    Result of the DescribeFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHumanTaskUi
```
public DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest request)
```
    Returns information about the requested human task user interface (worker task template).
    
    Specified by:
    
    describeHumanTaskUi in interface AmazonSageMaker
    
    Parameters:
    
    describeHumanTaskUiRequest -
    
    Returns:
    
    Result of the DescribeHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHyperParameterTuningJob
```
public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
```
    Gets a description of a hyperparameter tuning job.
    
    Specified by:
    
    describeHyperParameterTuningJob in interface AmazonSageMaker
    
    Parameters:
    
    describeHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the DescribeHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImage
```
public DescribeImageResult describeImage(DescribeImageRequest request)
```
    Describes a SageMaker image.
    
    Specified by:
    
    describeImage in interface AmazonSageMaker
    
    Parameters:
    
    describeImageRequest -
    
    Returns:
    
    Result of the DescribeImage operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImageVersion
```
public DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest request)
```
    Describes a version of a SageMaker image.
    
    Specified by:
    
    describeImageVersion in interface AmazonSageMaker
    
    Parameters:
    
    describeImageVersionRequest -
    
    Returns:
    
    Result of the DescribeImageVersion operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeInferenceRecommendationsJob
```
public DescribeInferenceRecommendationsJobResult describeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest request)
```
    Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
    
    Specified by:
    
    describeInferenceRecommendationsJob in interface AmazonSageMaker
    
    Parameters:
    
    describeInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the DescribeInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeLabelingJob
```
public DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest request)
```
    Gets information about a labeling job.
    
    Specified by:
    
    describeLabelingJob in interface AmazonSageMaker
    
    Parameters:
    
    describeLabelingJobRequest -
    
    Returns:
    
    Result of the DescribeLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeLineageGroup
```
public DescribeLineageGroupResult describeLineageGroup(DescribeLineageGroupRequest request)
```
    Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
    
    Specified by:
    
    describeLineageGroup in interface AmazonSageMaker
    
    Parameters:
    
    describeLineageGroupRequest -
    
    Returns:
    
    Result of the DescribeLineageGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModel
```
public DescribeModelResult describeModel(DescribeModelRequest request)
```
    Describes a model that you created using the CreateModel API.
    
    Specified by:
    
    describeModel in interface AmazonSageMaker
    
    Parameters:
    
    describeModelRequest -
    
    Returns:
    
    Result of the DescribeModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelBiasJobDefinition
```
public DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest request)
```
    Returns a description of a model bias job definition.
    
    Specified by:
    
    describeModelBiasJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    describeModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelExplainabilityJobDefinition
```
public DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest request)
```
    Returns a description of a model explainability job definition.
    
    Specified by:
    
    describeModelExplainabilityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    describeModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackage
```
public DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest request)
```
    Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
    
    To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
    
    Specified by:
    
    describeModelPackage in interface AmazonSageMaker
    
    Parameters:
    
    describeModelPackageRequest -
    
    Returns:
    
    Result of the DescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackageGroup
```
public DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest request)
```
    Gets a description for the specified model group.
    
    Specified by:
    
    describeModelPackageGroup in interface AmazonSageMaker
    
    Parameters:
    
    describeModelPackageGroupRequest -
    
    Returns:
    
    Result of the DescribeModelPackageGroup operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelQualityJobDefinition
```
public DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest request)
```
    Returns a description of a model quality job definition.
    
    Specified by:
    
    describeModelQualityJobDefinition in interface AmazonSageMaker
    
    Parameters:
    
    describeModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeMonitoringSchedule
```
public DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)
```
    Describes the schedule for a monitoring job.
    
    Specified by:
    
    describeMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    describeMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DescribeMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstance
```
public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
```
    Returns information about a notebook instance.
    
    Specified by:
    
    describeNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    describeNotebookInstanceRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstanceLifecycleConfig
```
public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
```
    Returns a description of a notebook instance lifecycle configuration.
    
    For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
    
    Specified by:
    
    describeNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    describeNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describePipeline
```
public DescribePipelineResult describePipeline(DescribePipelineRequest request)
```
    Describes the details of a pipeline.
    
    Specified by:
    
    describePipeline in interface AmazonSageMaker
    
    Parameters:
    
    describePipelineRequest -
    
    Returns:
    
    Result of the DescribePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describePipelineDefinitionForExecution
```
public DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest request)
```
    Describes the details of an execution's pipeline definition.
    
    Specified by:
    
    describePipelineDefinitionForExecution in interface AmazonSageMaker
    
    Parameters:
    
    describePipelineDefinitionForExecutionRequest -
    
    Returns:
    
    Result of the DescribePipelineDefinitionForExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describePipelineExecution
```
public DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest request)
```
    Describes the details of a pipeline execution.
    
    Specified by:
    
    describePipelineExecution in interface AmazonSageMaker
    
    Parameters:
    
    describePipelineExecutionRequest -
    
    Returns:
    
    Result of the DescribePipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeProcessingJob
```
public DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest request)
```
    Returns a description of a processing job.
    
    Specified by:
    
    describeProcessingJob in interface AmazonSageMaker
    
    Parameters:
    
    describeProcessingJobRequest -
    
    Returns:
    
    Result of the DescribeProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeProject
```
public DescribeProjectResult describeProject(DescribeProjectRequest request)
```
    Describes the details of a project.
    
    Specified by:
    
    describeProject in interface AmazonSageMaker
    
    Parameters:
    
    describeProjectRequest -
    
    Returns:
    
    Result of the DescribeProject operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeStudioLifecycleConfig
```
public DescribeStudioLifecycleConfigResult describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest request)
```
    Describes the Studio Lifecycle Configuration.
    
    Specified by:
    
    describeStudioLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    describeStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the DescribeStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeSubscribedWorkteam
```
public DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)
```
    Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
    
    Specified by:
    
    describeSubscribedWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    describeSubscribedWorkteamRequest -
    
    Returns:
    
    Result of the DescribeSubscribedWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrainingJob
```
public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
```
    Returns information about a training job.
    
    Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
    
    Specified by:
    
    describeTrainingJob in interface AmazonSageMaker
    
    Parameters:
    
    describeTrainingJobRequest -
    
    Returns:
    
    Result of the DescribeTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTransformJob
```
public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
```
    Returns information about a transform job.
    
    Specified by:
    
    describeTransformJob in interface AmazonSageMaker
    
    Parameters:
    
    describeTransformJobRequest -
    
    Returns:
    
    Result of the DescribeTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrial
```
public DescribeTrialResult describeTrial(DescribeTrialRequest request)
```
    Provides a list of a trial's properties.
    
    Specified by:
    
    describeTrial in interface AmazonSageMaker
    
    Parameters:
    
    describeTrialRequest -
    
    Returns:
    
    Result of the DescribeTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrialComponent
```
public DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest request)
```
    Provides a list of a trials component's properties.
    
    Specified by:
    
    describeTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    describeTrialComponentRequest -
    
    Returns:
    
    Result of the DescribeTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeUserProfile
```
public DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest request)
```
    Describes a user profile. For more information, see CreateUserProfile.
    
    Specified by:
    
    describeUserProfile in interface AmazonSageMaker
    
    Parameters:
    
    describeUserProfileRequest -
    
    Returns:
    
    Result of the DescribeUserProfile operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeWorkforce
```
public DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest request)
```
    Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
    
    This operation applies only to private workforces.
    
    Specified by:
    
    describeWorkforce in interface AmazonSageMaker
    
    Parameters:
    
    describeWorkforceRequest -
    
    Returns:
    
    Result of the DescribeWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeWorkteam
```
public DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest request)
```
    Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
    
    Specified by:
    
    describeWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    describeWorkteamRequest -
    
    Returns:
    
    Result of the DescribeWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disableSagemakerServicecatalogPortfolio
```
public DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest request)
```
    Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Specified by:
    
    disableSagemakerServicecatalogPortfolio in interface AmazonSageMaker
    
    Parameters:
    
    disableSagemakerServicecatalogPortfolioRequest -
    
    Returns:
    
    Result of the DisableSagemakerServicecatalogPortfolio operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disassociateTrialComponent
```
public DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest request)
```
    Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
    
    To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
    
    Specified by:
    
    disassociateTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    disassociateTrialComponentRequest -
    
    Returns:
    
    Result of the DisassociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - enableSagemakerServicecatalogPortfolio
```
public EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest request)
```
    Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Specified by:
    
    enableSagemakerServicecatalogPortfolio in interface AmazonSageMaker
    
    Parameters:
    
    enableSagemakerServicecatalogPortfolioRequest -
    
    Returns:
    
    Result of the EnableSagemakerServicecatalogPortfolio operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getDeviceFleetReport
```
public GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest request)
```
    Describes a fleet.
    
    Specified by:
    
    getDeviceFleetReport in interface AmazonSageMaker
    
    Parameters:
    
    getDeviceFleetReportRequest -
    
    Returns:
    
    Result of the GetDeviceFleetReport operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getLineageGroupPolicy
```
public GetLineageGroupPolicyResult getLineageGroupPolicy(GetLineageGroupPolicyRequest request)
```
    The resource policy for the lineage group.
    
    Specified by:
    
    getLineageGroupPolicy in interface AmazonSageMaker
    
    Parameters:
    
    getLineageGroupPolicyRequest -
    
    Returns:
    
    Result of the GetLineageGroupPolicy operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - getModelPackageGroupPolicy
```
public GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest request)
```
    Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
    
    Specified by:
    
    getModelPackageGroupPolicy in interface AmazonSageMaker
    
    Parameters:
    
    getModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the GetModelPackageGroupPolicy operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getSagemakerServicecatalogPortfolioStatus
```
public GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest request)
```
    Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Specified by:
    
    getSagemakerServicecatalogPortfolioStatus in interface AmazonSageMaker
    
    Parameters:
    
    getSagemakerServicecatalogPortfolioStatusRequest -
    
    Returns:
    
    Result of the GetSagemakerServicecatalogPortfolioStatus operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getSearchSuggestions
```
public GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest request)
```
    An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
    
    Specified by:
    
    getSearchSuggestions in interface AmazonSageMaker
    
    Parameters:
    
    getSearchSuggestionsRequest -
    
    Returns:
    
    Result of the GetSearchSuggestions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listActions
```
public ListActionsResult listActions(ListActionsRequest request)
```
    Lists the actions in your account and their properties.
    
    Specified by:
    
    listActions in interface AmazonSageMaker
    
    Parameters:
    
    listActionsRequest -
    
    Returns:
    
    Result of the ListActions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAlgorithms
```
public ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest request)
```
    Lists the machine learning algorithms that have been created.
    
    Specified by:
    
    listAlgorithms in interface AmazonSageMaker
    
    Parameters:
    
    listAlgorithmsRequest -
    
    Returns:
    
    Result of the ListAlgorithms operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAppImageConfigs
```
public ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest request)
```
    Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
    
    Specified by:
    
    listAppImageConfigs in interface AmazonSageMaker
    
    Parameters:
    
    listAppImageConfigsRequest -
    
    Returns:
    
    Result of the ListAppImageConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listApps
```
public ListAppsResult listApps(ListAppsRequest request)
```
    Lists apps.
    
    Specified by:
    
    listApps in interface AmazonSageMaker
    
    Parameters:
    
    listAppsRequest -
    
    Returns:
    
    Result of the ListApps operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listArtifacts
```
public ListArtifactsResult listArtifacts(ListArtifactsRequest request)
```
    Lists the artifacts in your account and their properties.
    
    Specified by:
    
    listArtifacts in interface AmazonSageMaker
    
    Parameters:
    
    listArtifactsRequest -
    
    Returns:
    
    Result of the ListArtifacts operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAssociations
```
public ListAssociationsResult listAssociations(ListAssociationsRequest request)
```
    Lists the associations in your account and their properties.
    
    Specified by:
    
    listAssociations in interface AmazonSageMaker
    
    Parameters:
    
    listAssociationsRequest -
    
    Returns:
    
    Result of the ListAssociations operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAutoMLJobs
```
public ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest request)
```
    Request a list of jobs.
    
    Specified by:
    
    listAutoMLJobs in interface AmazonSageMaker
    
    Parameters:
    
    listAutoMLJobsRequest -
    
    Returns:
    
    Result of the ListAutoMLJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCandidatesForAutoMLJob
```
public ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)
```
    List the candidates created for the job.
    
    Specified by:
    
    listCandidatesForAutoMLJob in interface AmazonSageMaker
    
    Parameters:
    
    listCandidatesForAutoMLJobRequest -
    
    Returns:
    
    Result of the ListCandidatesForAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listCodeRepositories
```
public ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest request)
```
    Gets a list of the Git repositories in your account.
    
    Specified by:
    
    listCodeRepositories in interface AmazonSageMaker
    
    Parameters:
    
    listCodeRepositoriesRequest -
    
    Returns:
    
    Result of the ListCodeRepositories operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCompilationJobs
```
public ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest request)
```
    Lists model compilation jobs that satisfy various filters.
    
    To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
    
    Specified by:
    
    listCompilationJobs in interface AmazonSageMaker
    
    Parameters:
    
    listCompilationJobsRequest -
    
    Returns:
    
    Result of the ListCompilationJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listContexts
```
public ListContextsResult listContexts(ListContextsRequest request)
```
    Lists the contexts in your account and their properties.
    
    Specified by:
    
    listContexts in interface AmazonSageMaker
    
    Parameters:
    
    listContextsRequest -
    
    Returns:
    
    Result of the ListContexts operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listDataQualityJobDefinitions
```
public ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest request)
```
    Lists the data quality job definitions in your account.
    
    Specified by:
    
    listDataQualityJobDefinitions in interface AmazonSageMaker
    
    Parameters:
    
    listDataQualityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListDataQualityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDeviceFleets
```
public ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest request)
```
    Returns a list of devices in the fleet.
    
    Specified by:
    
    listDeviceFleets in interface AmazonSageMaker
    
    Parameters:
    
    listDeviceFleetsRequest -
    
    Returns:
    
    Result of the ListDeviceFleets operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDevices
```
public ListDevicesResult listDevices(ListDevicesRequest request)
```
    A list of devices.
    
    Specified by:
    
    listDevices in interface AmazonSageMaker
    
    Parameters:
    
    listDevicesRequest -
    
    Returns:
    
    Result of the ListDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDomains
```
public ListDomainsResult listDomains(ListDomainsRequest request)
```
    Lists the domains.
    
    Specified by:
    
    listDomains in interface AmazonSageMaker
    
    Parameters:
    
    listDomainsRequest -
    
    Returns:
    
    Result of the ListDomains operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEdgePackagingJobs
```
public ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest request)
```
    Returns a list of edge packaging jobs.
    
    Specified by:
    
    listEdgePackagingJobs in interface AmazonSageMaker
    
    Parameters:
    
    listEdgePackagingJobsRequest -
    
    Returns:
    
    Result of the ListEdgePackagingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpointConfigs
```
public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
```
    Lists endpoint configurations.
    
    Specified by:
    
    listEndpointConfigs in interface AmazonSageMaker
    
    Parameters:
    
    listEndpointConfigsRequest -
    
    Returns:
    
    Result of the ListEndpointConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpoints
```
public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
```
    Lists endpoints.
    
    Specified by:
    
    listEndpoints in interface AmazonSageMaker
    
    Parameters:
    
    listEndpointsRequest -
    
    Returns:
    
    Result of the ListEndpoints operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listExperiments
```
public ListExperimentsResult listExperiments(ListExperimentsRequest request)
```
    Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
    
    Specified by:
    
    listExperiments in interface AmazonSageMaker
    
    Parameters:
    
    listExperimentsRequest -
    
    Returns:
    
    Result of the ListExperiments operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFeatureGroups
```
public ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest request)
```
    List FeatureGroups based on given filter and order.
    
    Specified by:
    
    listFeatureGroups in interface AmazonSageMaker
    
    Parameters:
    
    listFeatureGroupsRequest -
    
    Returns:
    
    Result of the ListFeatureGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFlowDefinitions
```
public ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest request)
```
    Returns information about the flow definitions in your account.
    
    Specified by:
    
    listFlowDefinitions in interface AmazonSageMaker
    
    Parameters:
    
    listFlowDefinitionsRequest -
    
    Returns:
    
    Result of the ListFlowDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHumanTaskUis
```
public ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest request)
```
    Returns information about the human task user interfaces in your account.
    
    Specified by:
    
    listHumanTaskUis in interface AmazonSageMaker
    
    Parameters:
    
    listHumanTaskUisRequest -
    
    Returns:
    
    Result of the ListHumanTaskUis operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHyperParameterTuningJobs
```
public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
```
    Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
    
    Specified by:
    
    listHyperParameterTuningJobs in interface AmazonSageMaker
    
    Parameters:
    
    listHyperParameterTuningJobsRequest -
    
    Returns:
    
    Result of the ListHyperParameterTuningJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listImageVersions
```
public ListImageVersionsResult listImageVersions(ListImageVersionsRequest request)
```
    Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
    
    Specified by:
    
    listImageVersions in interface AmazonSageMaker
    
    Parameters:
    
    listImageVersionsRequest -
    
    Returns:
    
    Result of the ListImageVersions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listImages
```
public ListImagesResult listImages(ListImagesRequest request)
```
    Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
    
    Specified by:
    
    listImages in interface AmazonSageMaker
    
    Parameters:
    
    listImagesRequest -
    
    Returns:
    
    Result of the ListImages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listInferenceRecommendationsJobs
```
public ListInferenceRecommendationsJobsResult listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest request)
```
    Lists recommendation jobs that satisfy various filters.
    
    Specified by:
    
    listInferenceRecommendationsJobs in interface AmazonSageMaker
    
    Parameters:
    
    listInferenceRecommendationsJobsRequest -
    
    Returns:
    
    Result of the ListInferenceRecommendationsJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobs
```
public ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest request)
```
    Gets a list of labeling jobs.
    
    Specified by:
    
    listLabelingJobs in interface AmazonSageMaker
    
    Parameters:
    
    listLabelingJobsRequest -
    
    Returns:
    
    Result of the ListLabelingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobsForWorkteam
```
public ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)
```
    Gets a list of labeling jobs assigned to a specified work team.
    
    Specified by:
    
    listLabelingJobsForWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    listLabelingJobsForWorkteamRequest -
    
    Returns:
    
    Result of the ListLabelingJobsForWorkteam operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listLineageGroups
```
public ListLineageGroupsResult listLineageGroups(ListLineageGroupsRequest request)
```
    A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
    
    Specified by:
    
    listLineageGroups in interface AmazonSageMaker
    
    Parameters:
    
    listLineageGroupsRequest -
    
    Returns:
    
    Result of the ListLineageGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelBiasJobDefinitions
```
public ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest request)
```
    Lists model bias jobs definitions that satisfy various filters.
    
    Specified by:
    
    listModelBiasJobDefinitions in interface AmazonSageMaker
    
    Parameters:
    
    listModelBiasJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelBiasJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelExplainabilityJobDefinitions
```
public ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest request)
```
    Lists model explainability job definitions that satisfy various filters.
    
    Specified by:
    
    listModelExplainabilityJobDefinitions in interface AmazonSageMaker
    
    Parameters:
    
    listModelExplainabilityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelExplainabilityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelMetadata
```
public ListModelMetadataResult listModelMetadata(ListModelMetadataRequest request)
```
    Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
    
    Specified by:
    
    listModelMetadata in interface AmazonSageMaker
    
    Parameters:
    
    listModelMetadataRequest -
    
    Returns:
    
    Result of the ListModelMetadata operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelPackageGroups
```
public ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest request)
```
    Gets a list of the model groups in your Amazon Web Services account.
    
    Specified by:
    
    listModelPackageGroups in interface AmazonSageMaker
    
    Parameters:
    
    listModelPackageGroupsRequest -
    
    Returns:
    
    Result of the ListModelPackageGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelPackages
```
public ListModelPackagesResult listModelPackages(ListModelPackagesRequest request)
```
    Lists the model packages that have been created.
    
    Specified by:
    
    listModelPackages in interface AmazonSageMaker
    
    Parameters:
    
    listModelPackagesRequest -
    
    Returns:
    
    Result of the ListModelPackages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelQualityJobDefinitions
```
public ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest request)
```
    Gets a list of model quality monitoring job definitions in your account.
    
    Specified by:
    
    listModelQualityJobDefinitions in interface AmazonSageMaker
    
    Parameters:
    
    listModelQualityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelQualityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModels
```
public ListModelsResult listModels(ListModelsRequest request)
```
    Lists models created with the CreateModel API.
    
    Specified by:
    
    listModels in interface AmazonSageMaker
    
    Parameters:
    
    listModelsRequest -
    
    Returns:
    
    Result of the ListModels operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringExecutions
```
public ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest request)
```
    Returns list of all monitoring job executions.
    
    Specified by:
    
    listMonitoringExecutions in interface AmazonSageMaker
    
    Parameters:
    
    listMonitoringExecutionsRequest -
    
    Returns:
    
    Result of the ListMonitoringExecutions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringSchedules
```
public ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest request)
```
    Returns list of all monitoring schedules.
    
    Specified by:
    
    listMonitoringSchedules in interface AmazonSageMaker
    
    Parameters:
    
    listMonitoringSchedulesRequest -
    
    Returns:
    
    Result of the ListMonitoringSchedules operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstanceLifecycleConfigs
```
public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
```
    Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    listNotebookInstanceLifecycleConfigs in interface AmazonSageMaker
    
    Parameters:
    
    listNotebookInstanceLifecycleConfigsRequest -
    
    Returns:
    
    Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstances
```
public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
```
    Returns a list of the Amazon SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
    
    Specified by:
    
    listNotebookInstances in interface AmazonSageMaker
    
    Parameters:
    
    listNotebookInstancesRequest -
    
    Returns:
    
    Result of the ListNotebookInstances operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listPipelineExecutionSteps
```
public ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest request)
```
    Gets a list of PipeLineExecutionStep objects.
    
    Specified by:
    
    listPipelineExecutionSteps in interface AmazonSageMaker
    
    Parameters:
    
    listPipelineExecutionStepsRequest -
    
    Returns:
    
    Result of the ListPipelineExecutionSteps operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelineExecutions
```
public ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest request)
```
    Gets a list of the pipeline executions.
    
    Specified by:
    
    listPipelineExecutions in interface AmazonSageMaker
    
    Parameters:
    
    listPipelineExecutionsRequest -
    
    Returns:
    
    Result of the ListPipelineExecutions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelineParametersForExecution
```
public ListPipelineParametersForExecutionResult listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest request)
```
    Gets a list of parameters for a pipeline execution.
    
    Specified by:
    
    listPipelineParametersForExecution in interface AmazonSageMaker
    
    Parameters:
    
    listPipelineParametersForExecutionRequest -
    
    Returns:
    
    Result of the ListPipelineParametersForExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelines
```
public ListPipelinesResult listPipelines(ListPipelinesRequest request)
```
    Gets a list of pipelines.
    
    Specified by:
    
    listPipelines in interface AmazonSageMaker
    
    Parameters:
    
    listPipelinesRequest -
    
    Returns:
    
    Result of the ListPipelines operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProcessingJobs
```
public ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest request)
```
    Lists processing jobs that satisfy various filters.
    
    Specified by:
    
    listProcessingJobs in interface AmazonSageMaker
    
    Parameters:
    
    listProcessingJobsRequest -
    
    Returns:
    
    Result of the ListProcessingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProjects
```
public ListProjectsResult listProjects(ListProjectsRequest request)
```
    Gets a list of the projects in an Amazon Web Services account.
    
    Specified by:
    
    listProjects in interface AmazonSageMaker
    
    Parameters:
    
    listProjectsRequest -
    
    Returns:
    
    Result of the ListProjects operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listStudioLifecycleConfigs
```
public ListStudioLifecycleConfigsResult listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest request)
```
    Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.
    
    Specified by:
    
    listStudioLifecycleConfigs in interface AmazonSageMaker
    
    Parameters:
    
    listStudioLifecycleConfigsRequest -
    
    Returns:
    
    Result of the ListStudioLifecycleConfigs operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - listSubscribedWorkteams
```
public ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)
```
    Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Specified by:
    
    listSubscribedWorkteams in interface AmazonSageMaker
    
    Parameters:
    
    listSubscribedWorkteamsRequest -
    
    Returns:
    
    Result of the ListSubscribedWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTags
```
public ListTagsResult listTags(ListTagsRequest request)
```
    Returns the tags for the specified Amazon SageMaker resource.
    
    Specified by:
    
    listTags in interface AmazonSageMaker
    
    Parameters:
    
    listTagsRequest -
    
    Returns:
    
    Result of the ListTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobs
```
public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
```
    Lists training jobs.
    
    When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.
    
    For example, if ListTrainingJobs is invoked with the following parameters:
    
    { ... MaxResults: 100, StatusEquals: InProgress ... }
    
    First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.
    
    You can quickly test the API using the following Amazon Web Services CLI code.
    
    aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
    
    Specified by:
    
    listTrainingJobs in interface AmazonSageMaker
    
    Parameters:
    
    listTrainingJobsRequest -
    
    Returns:
    
    Result of the ListTrainingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobsForHyperParameterTuningJob
```
public ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
```
    Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
    
    Specified by:
    
    listTrainingJobsForHyperParameterTuningJob in interface AmazonSageMaker
    
    Parameters:
    
    listTrainingJobsForHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTransformJobs
```
public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
```
    Lists transform jobs.
    
    Specified by:
    
    listTransformJobs in interface AmazonSageMaker
    
    Parameters:
    
    listTransformJobsRequest -
    
    Returns:
    
    Result of the ListTransformJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrialComponents
```
public ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest request)
```
    Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
    - ExperimentName
    - SourceArn
    - TrialName
    Specified by:
    
    listTrialComponents in interface AmazonSageMaker
    
    Parameters:
    
    listTrialComponentsRequest -
    
    Returns:
    
    Result of the ListTrialComponents operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTrials
```
public ListTrialsResult listTrials(ListTrialsRequest request)
```
    Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
    
    Specified by:
    
    listTrials in interface AmazonSageMaker
    
    Parameters:
    
    listTrialsRequest -
    
    Returns:
    
    Result of the ListTrials operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listUserProfiles
```
public ListUserProfilesResult listUserProfiles(ListUserProfilesRequest request)
```
    Lists user profiles.
    
    Specified by:
    
    listUserProfiles in interface AmazonSageMaker
    
    Parameters:
    
    listUserProfilesRequest -
    
    Returns:
    
    Result of the ListUserProfiles operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkforces
```
public ListWorkforcesResult listWorkforces(ListWorkforcesRequest request)
```
    Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
    
    Specified by:
    
    listWorkforces in interface AmazonSageMaker
    
    Parameters:
    
    listWorkforcesRequest -
    
    Returns:
    
    Result of the ListWorkforces operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkteams
```
public ListWorkteamsResult listWorkteams(ListWorkteamsRequest request)
```
    Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Specified by:
    
    listWorkteams in interface AmazonSageMaker
    
    Parameters:
    
    listWorkteamsRequest -
    
    Returns:
    
    Result of the ListWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - putModelPackageGroupPolicy
```
public PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest request)
```
    Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
    
    Specified by:
    
    putModelPackageGroupPolicy in interface AmazonSageMaker
    
    Parameters:
    
    putModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the PutModelPackageGroupPolicy operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - queryLineage
```
public QueryLineageResult queryLineage(QueryLineageRequest request)
```
    Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
    
    Specified by:
    
    queryLineage in interface AmazonSageMaker
    
    Parameters:
    
    queryLineageRequest -
    
    Returns:
    
    Result of the QueryLineage operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - registerDevices
```
public RegisterDevicesResult registerDevices(RegisterDevicesRequest request)
```
    Register devices.
    
    Specified by:
    
    registerDevices in interface AmazonSageMaker
    
    Parameters:
    
    registerDevicesRequest -
    
    Returns:
    
    Result of the RegisterDevices operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - renderUiTemplate
```
public RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest request)
```
    Renders the UI template so that you can preview the worker's experience.
    
    Specified by:
    
    renderUiTemplate in interface AmazonSageMaker
    
    Parameters:
    
    renderUiTemplateRequest -
    
    Returns:
    
    Result of the RenderUiTemplate operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - retryPipelineExecution
```
public RetryPipelineExecutionResult retryPipelineExecution(RetryPipelineExecutionRequest request)
```
    Retry the execution of the pipeline.
    
    Specified by:
    
    retryPipelineExecution in interface AmazonSageMaker
    
    Parameters:
    
    retryPipelineExecutionRequest -
    
    Returns:
    
    Result of the RetryPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - search
```
public SearchResult search(SearchRequest request)
```
    Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
    
    You can query against the following value types: numeric, text, Boolean, and timestamp.
    
    Specified by:
    
    search in interface AmazonSageMaker
    
    Parameters:
    
    searchRequest -
    
    Returns:
    
    Result of the Search operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - sendPipelineExecutionStepFailure
```
public SendPipelineExecutionStepFailureResult sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest request)
```
    Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
    
    Specified by:
    
    sendPipelineExecutionStepFailure in interface AmazonSageMaker
    
    Parameters:
    
    sendPipelineExecutionStepFailureRequest -
    
    Returns:
    
    Result of the SendPipelineExecutionStepFailure operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - sendPipelineExecutionStepSuccess
```
public SendPipelineExecutionStepSuccessResult sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest request)
```
    Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
    
    Specified by:
    
    sendPipelineExecutionStepSuccess in interface AmazonSageMaker
    
    Parameters:
    
    sendPipelineExecutionStepSuccessRequest -
    
    Returns:
    
    Result of the SendPipelineExecutionStepSuccess operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - startMonitoringSchedule
```
public StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest request)
```
    Starts a previously stopped monitoring schedule.
    
    By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.
    
    Specified by:
    
    startMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    startMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StartMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - startNotebookInstance
```
public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
```
    Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.
    
    Specified by:
    
    startNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    startNotebookInstanceRequest -
    
    Returns:
    
    Result of the StartNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - startPipelineExecution
```
public StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest request)
```
    Starts a pipeline execution.
    
    Specified by:
    
    startPipelineExecution in interface AmazonSageMaker
    
    Parameters:
    
    startPipelineExecutionRequest -
    
    Returns:
    
    Result of the StartPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - stopAutoMLJob
```
public StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest request)
```
    A method for forcing the termination of a running job.
    
    Specified by:
    
    stopAutoMLJob in interface AmazonSageMaker
    
    Parameters:
    
    stopAutoMLJobRequest -
    
    Returns:
    
    Result of the StopAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopCompilationJob
```
public StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest request)
```
    Stops a model compilation job.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
    
    When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
    
    Specified by:
    
    stopCompilationJob in interface AmazonSageMaker
    
    Parameters:
    
    stopCompilationJobRequest -
    
    Returns:
    
    Result of the StopCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopEdgePackagingJob
```
public StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest request)
```
    Request to stop an edge packaging job.
    
    Specified by:
    
    stopEdgePackagingJob in interface AmazonSageMaker
    
    Parameters:
    
    stopEdgePackagingJobRequest -
    
    Returns:
    
    Result of the StopEdgePackagingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopHyperParameterTuningJob
```
public StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
```
    Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
    
    All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
    
    Specified by:
    
    stopHyperParameterTuningJob in interface AmazonSageMaker
    
    Parameters:
    
    stopHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the StopHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopInferenceRecommendationsJob
```
public StopInferenceRecommendationsJobResult stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest request)
```
    Stops an Inference Recommender job.
    
    Specified by:
    
    stopInferenceRecommendationsJob in interface AmazonSageMaker
    
    Parameters:
    
    stopInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the StopInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopLabelingJob
```
public StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest request)
```
    Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
    
    Specified by:
    
    stopLabelingJob in interface AmazonSageMaker
    
    Parameters:
    
    stopLabelingJobRequest -
    
    Returns:
    
    Result of the StopLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopMonitoringSchedule
```
public StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest request)
```
    Stops a previously started monitoring schedule.
    
    Specified by:
    
    stopMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    stopMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StopMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopNotebookInstance
```
public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
```
    Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.
    
    To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.
    
    Specified by:
    
    stopNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    stopNotebookInstanceRequest -
    
    Returns:
    
    Result of the StopNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopPipelineExecution
```
public StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest request)
```
    Stops a pipeline execution.
    
    Callback Step
    
    A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".
    
    You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.
    
    Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
    
    Lambda Step
    
    A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
    
    Specified by:
    
    stopPipelineExecution in interface AmazonSageMaker
    
    Parameters:
    
    stopPipelineExecutionRequest -
    
    Returns:
    
    Result of the StopPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopProcessingJob
```
public StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest request)
```
    Stops a processing job.
    
    Specified by:
    
    stopProcessingJob in interface AmazonSageMaker
    
    Parameters:
    
    stopProcessingJobRequest -
    
    Returns:
    
    Result of the StopProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTrainingJob
```
public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
```
    Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.
    
    When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.
    
    Specified by:
    
    stopTrainingJob in interface AmazonSageMaker
    
    Parameters:
    
    stopTrainingJobRequest -
    
    Returns:
    
    Result of the StopTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTransformJob
```
public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
```
    Stops a batch transform job.
    
    When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
    
    Specified by:
    
    stopTransformJob in interface AmazonSageMaker
    
    Parameters:
    
    stopTransformJobRequest -
    
    Returns:
    
    Result of the StopTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateAction
```
public UpdateActionResult updateAction(UpdateActionRequest request)
```
    Updates an action.
    
    Specified by:
    
    updateAction in interface AmazonSageMaker
    
    Parameters:
    
    updateActionRequest -
    
    Returns:
    
    Result of the UpdateAction operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateAppImageConfig
```
public UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest request)
```
    Updates the properties of an AppImageConfig.
    
    Specified by:
    
    updateAppImageConfig in interface AmazonSageMaker
    
    Parameters:
    
    updateAppImageConfigRequest -
    
    Returns:
    
    Result of the UpdateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateArtifact
```
public UpdateArtifactResult updateArtifact(UpdateArtifactRequest request)
```
    Updates an artifact.
    
    Specified by:
    
    updateArtifact in interface AmazonSageMaker
    
    Parameters:
    
    updateArtifactRequest -
    
    Returns:
    
    Result of the UpdateArtifact operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateCodeRepository
```
public UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest request)
```
    Updates the specified Git repository with the specified values.
    
    Specified by:
    
    updateCodeRepository in interface AmazonSageMaker
    
    Parameters:
    
    updateCodeRepositoryRequest -
    
    Returns:
    
    Result of the UpdateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateContext
```
public UpdateContextResult updateContext(UpdateContextRequest request)
```
    Updates a context.
    
    Specified by:
    
    updateContext in interface AmazonSageMaker
    
    Parameters:
    
    updateContextRequest -
    
    Returns:
    
    Result of the UpdateContext operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateDeviceFleet
```
public UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest request)
```
    Updates a fleet of devices.
    
    Specified by:
    
    updateDeviceFleet in interface AmazonSageMaker
    
    Parameters:
    
    updateDeviceFleetRequest -
    
    Returns:
    
    Result of the UpdateDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - updateDevices
```
public UpdateDevicesResult updateDevices(UpdateDevicesRequest request)
```
    Updates one or more devices in a fleet.
    
    Specified by:
    
    updateDevices in interface AmazonSageMaker
    
    Parameters:
    
    updateDevicesRequest -
    
    Returns:
    
    Result of the UpdateDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateDomain
```
public UpdateDomainResult updateDomain(UpdateDomainRequest request)
```
    Updates the default settings for new user profiles in the domain.
    
    Specified by:
    
    updateDomain in interface AmazonSageMaker
    
    Parameters:
    
    updateDomainRequest -
    
    Returns:
    
    Result of the UpdateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateEndpoint
```
public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
```
    Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
    
    When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
    
    You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
    
    If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
    
    Specified by:
    
    updateEndpoint in interface AmazonSageMaker
    
    Parameters:
    
    updateEndpointRequest -
    
    Returns:
    
    Result of the UpdateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateEndpointWeightsAndCapacities
```
public UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
```
    Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
    
    Specified by:
    
    updateEndpointWeightsAndCapacities in interface AmazonSageMaker
    
    Parameters:
    
    updateEndpointWeightsAndCapacitiesRequest -
    
    Returns:
    
    Result of the UpdateEndpointWeightsAndCapacities operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateExperiment
```
public UpdateExperimentResult updateExperiment(UpdateExperimentRequest request)
```
    Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
    
    Specified by:
    
    updateExperiment in interface AmazonSageMaker
    
    Parameters:
    
    updateExperimentRequest -
    
    Returns:
    
    Result of the UpdateExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateImage
```
public UpdateImageResult updateImage(UpdateImageRequest request)
```
    Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
    
    Specified by:
    
    updateImage in interface AmazonSageMaker
    
    Parameters:
    
    updateImageRequest -
    
    Returns:
    
    Result of the UpdateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateModelPackage
```
public UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest request)
```
    Updates a versioned model.
    
    Specified by:
    
    updateModelPackage in interface AmazonSageMaker
    
    Parameters:
    
    updateModelPackageRequest -
    
    Returns:
    
    Result of the UpdateModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateMonitoringSchedule
```
public UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)
```
    Updates a previously created schedule.
    
    Specified by:
    
    updateMonitoringSchedule in interface AmazonSageMaker
    
    Parameters:
    
    updateMonitoringScheduleRequest -
    
    Returns:
    
    Result of the UpdateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstance
```
public UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
```
    Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
    
    Specified by:
    
    updateNotebookInstance in interface AmazonSageMaker
    
    Parameters:
    
    updateNotebookInstanceRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstanceLifecycleConfig
```
public UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
```
    Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    updateNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Parameters:
    
    updateNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updatePipeline
```
public UpdatePipelineResult updatePipeline(UpdatePipelineRequest request)
```
    Updates a pipeline.
    
    Specified by:
    
    updatePipeline in interface AmazonSageMaker
    
    Parameters:
    
    updatePipelineRequest -
    
    Returns:
    
    Result of the UpdatePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updatePipelineExecution
```
public UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest request)
```
    Updates a pipeline execution.
    
    Specified by:
    
    updatePipelineExecution in interface AmazonSageMaker
    
    Parameters:
    
    updatePipelineExecutionRequest -
    
    Returns:
    
    Result of the UpdatePipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateProject
```
public UpdateProjectResult updateProject(UpdateProjectRequest request)
```
    Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
    
    You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.
    
    Specified by:
    
    updateProject in interface AmazonSageMaker
    
    Parameters:
    
    updateProjectRequest -
    
    Returns:
    
    Result of the UpdateProject operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateTrainingJob
```
public UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest request)
```
    Update a model training job to request a new Debugger profiling configuration.
    
    Specified by:
    
    updateTrainingJob in interface AmazonSageMaker
    
    Parameters:
    
    updateTrainingJobRequest -
    
    Returns:
    
    Result of the UpdateTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateTrial
```
public UpdateTrialResult updateTrial(UpdateTrialRequest request)
```
    Updates the display name of a trial.
    
    Specified by:
    
    updateTrial in interface AmazonSageMaker
    
    Parameters:
    
    updateTrialRequest -
    
    Returns:
    
    Result of the UpdateTrial operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateTrialComponent
```
public UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest request)
```
    Updates one or more properties of a trial component.
    
    Specified by:
    
    updateTrialComponent in interface AmazonSageMaker
    
    Parameters:
    
    updateTrialComponentRequest -
    
    Returns:
    
    Result of the UpdateTrialComponent operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateUserProfile
```
public UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest request)
```
    Updates a user profile.
    
    Specified by:
    
    updateUserProfile in interface AmazonSageMaker
    
    Parameters:
    
    updateUserProfileRequest -
    
    Returns:
    
    Result of the UpdateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateWorkforce
```
public UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest request)
```
    Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
    
    Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal.
    
    Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.
    
    You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.
    
    After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.
    
    This operation only applies to private workforces.
    
    Specified by:
    
    updateWorkforce in interface AmazonSageMaker
    
    Parameters:
    
    updateWorkforceRequest -
    
    Returns:
    
    Result of the UpdateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateWorkteam
```
public UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest request)
```
    Updates an existing work team with new member definitions or description.
    
    Specified by:
    
    updateWorkteam in interface AmazonSageMaker
    
    Parameters:
    
    updateWorkteamRequest -
    
    Returns:
    
    Result of the UpdateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - getCachedResponseMetadata
```
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Returns additional metadata for a previously executed successful, request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.
    Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing the request.
    
    Specified by:
    
    getCachedResponseMetadata in interface AmazonSageMaker
    
    Parameters:
    
    request - The originally executed request
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
public AmazonSageMakerWaiters waiters()
```
    Specified by:
    
    waiters in interface AmazonSageMaker
  - shutdown
```
public void shutdown()
```
    Description copied from class: AmazonWebServiceClient
    
    Shuts down this client object, releasing any resources that might be held open. If this method is not invoked, resources may be leaked. Once a client has been shutdown, it should not be used to make any more requests.
    
    Specified by:
    
    shutdown in interface AmazonSageMaker
    
    Overrides:
    
    shutdown in class AmazonWebServiceClient

Did this page help you?

Class AmazonSageMakerClient

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceClient

Fields inherited from interface com.amazonaws.services.sagemaker.AmazonSageMaker

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceClient

Methods inherited from class java.lang.Object

Method Detail

builder

addAssociation

addTags

associateTrialComponent

batchDescribeModelPackage

createAction

createAlgorithm

createApp

createAppImageConfig

createArtifact

createAutoMLJob

createCodeRepository

createCompilationJob

createContext

createDataQualityJobDefinition

createDeviceFleet

createDomain

createEdgePackagingJob

createEndpoint

createEndpointConfig

createExperiment

createFeatureGroup

createFlowDefinition

createHumanTaskUi

createHyperParameterTuningJob

createImage

createImageVersion

createInferenceRecommendationsJob

createLabelingJob

createModel

createModelBiasJobDefinition

createModelExplainabilityJobDefinition

createModelPackage

createModelPackageGroup

createModelQualityJobDefinition

createMonitoringSchedule

createNotebookInstance

createNotebookInstanceLifecycleConfig

createPipeline

createPresignedDomainUrl

createPresignedNotebookInstanceUrl

createProcessingJob

createProject

createStudioLifecycleConfig

createTrainingJob

createTransformJob

createTrial

createTrialComponent

createUserProfile

createWorkforce

createWorkteam

deleteAction

deleteAlgorithm

deleteApp

deleteAppImageConfig

deleteArtifact

deleteAssociation

deleteCodeRepository

deleteContext

deleteDataQualityJobDefinition

deleteDeviceFleet

deleteDomain

deleteEndpoint

deleteEndpointConfig

deleteExperiment

deleteFeatureGroup

deleteFlowDefinition

deleteHumanTaskUi

deleteImage

deleteImageVersion

deleteModel