AbstractAmazonSageMaker (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.AbstractAmazonSageMaker

All Implemented Interfaces:

AmazonSageMaker

Direct Known Subclasses:

AbstractAmazonSageMakerAsync
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AbstractAmazonSageMaker
extends Object
implements AmazonSageMaker
```
Abstract implementation of AmazonSageMaker. Convenient method forms pass through to the corresponding overload that takes a request object, which throws an UnsupportedOperationException.

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

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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.
`CreateAlgorithmResult`	`createAlgorithm(CreateAlgorithmRequest request)` Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
`CreateAppResult`	`createApp(CreateAppRequest request)` Creates a running App for the specified UserProfile.
`CreateAppImageConfigResult`	`createAppImageConfig(CreateAppImageConfigRequest request)` Creates a configuration for running an Amazon SageMaker image as a KernelGateway app.
`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.
`CreateDomainResult`	`createDomain(CreateDomainRequest request)` Creates a `Domain` used by Amazon SageMaker Studio.
`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.
`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 SageMaker `Image`.
`CreateImageVersionResult`	`createImageVersion(CreateImageVersionRequest request)` Creates a version of the SageMaker image specified by `ImageName`.
`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.
`CreateModelPackageResult`	`createModelPackage(CreateModelPackageRequest request)` Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace.
`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.
`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.
`CreateTrainingJobResult`	`createTrainingJob(CreateTrainingJobRequest request)` Starts a model training job.
`CreateTransformJobResult`	`createTransformJob(CreateTransformJobRequest request)` Starts a transform job.
`CreateTrialResult`	`createTrial(CreateTrialRequest request)` Creates an Amazon 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.
`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.
`DeleteCodeRepositoryResult`	`deleteCodeRepository(DeleteCodeRepositoryRequest request)` Deletes the specified Git repository from your account.
`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 Amazon SageMaker experiment.
`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.
`DeleteModelPackageResult`	`deleteModelPackage(DeleteModelPackageRequest request)` Deletes a model package.
`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.
`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.
`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.
`DescribeAutoMLJobResult`	`describeAutoMLJob(DescribeAutoMLJobRequest request)` Returns information about an Amazon SageMaker job.
`DescribeCodeRepositoryResult`	`describeCodeRepository(DescribeCodeRepositoryRequest request)` Gets details about the specified Git repository.
`DescribeCompilationJobResult`	`describeCompilationJob(DescribeCompilationJobRequest request)` Returns information about a model compilation job.
`DescribeDomainResult`	`describeDomain(DescribeDomainRequest request)` The description of the domain.
`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.
`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.
`DescribeLabelingJobResult`	`describeLabelingJob(DescribeLabelingJobRequest request)` Gets information about a labeling job.
`DescribeModelResult`	`describeModel(DescribeModelRequest request)` Describes a model that you created using the `CreateModel` API.
`DescribeModelPackageResult`	`describeModelPackage(DescribeModelPackageRequest request)` Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
`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.
`DescribeProcessingJobResult`	`describeProcessingJob(DescribeProcessingJobRequest request)` Returns a description of a processing job.
`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.
`DisassociateTrialComponentResult`	`disassociateTrialComponent(DisassociateTrialComponentRequest request)` Disassociates a trial component from a trial.
`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.
`GetSearchSuggestionsResult`	`getSearchSuggestions(GetSearchSuggestionsRequest request)` An auto-complete API for the search functionality in the Amazon SageMaker console.
`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.
`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.
`ListDomainsResult`	`listDomains(ListDomainsRequest request)` Lists the domains.
`ListEndpointConfigsResult`	`listEndpointConfigs(ListEndpointConfigsRequest request)` Lists endpoint configurations.
`ListEndpointsResult`	`listEndpoints(ListEndpointsRequest request)` Lists endpoints.
`ListExperimentsResult`	`listExperiments(ListExperimentsRequest request)` Lists all the experiments in your account.
`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.
`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.
`ListModelPackagesResult`	`listModelPackages(ListModelPackagesRequest request)` Lists the model packages that have been created.
`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 AWS Region.
`ListProcessingJobsResult`	`listProcessingJobs(ListProcessingJobsRequest request)` Lists processing jobs that satisfy various filters.
`ListSubscribedWorkteamsResult`	`listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)` Gets a list of the work teams that you are subscribed to in the AWS 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 AWS Region.
`ListWorkteamsResult`	`listWorkteams(ListWorkteamsRequest request)` Gets a list of private work teams that you have defined in a region.
`RenderUiTemplateResult`	`renderUiTemplate(RenderUiTemplateRequest request)` Renders the UI template so that you can preview the worker's experience.
`SearchResult`	`search(SearchRequest request)` Finds Amazon SageMaker resources that match a search query.
`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.
`StopAutoMLJobResult`	`stopAutoMLJob(StopAutoMLJobRequest request)` A method for forcing the termination of a running job.
`StopCompilationJobResult`	`stopCompilationJob(StopCompilationJobRequest request)` Stops a model compilation job.
`StopHyperParameterTuningJobResult`	`stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)` Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
`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.
`StopProcessingJobResult`	`stopProcessingJob(StopProcessingJobRequest request)` Stops a processing job.
`StopTrainingJobResult`	`stopTrainingJob(StopTrainingJobRequest request)` Stops a training job.
`StopTransformJobResult`	`stopTransformJob(StopTransformJobRequest request)` Stops a transform job.
`UpdateAppImageConfigResult`	`updateAppImageConfig(UpdateAppImageConfigRequest request)` Updates the properties of an AppImageConfig.
`UpdateCodeRepositoryResult`	`updateCodeRepository(UpdateCodeRepositoryRequest request)` Updates the specified Git repository with the specified values.
`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.
`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.
`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
  - addTags
```
public AddTagsResult addTags(AddTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 AWS 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
    
    Specified by:
    
    addTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - associateTrialComponent
```
public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the AssociateTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createAlgorithm
```
public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
    
    Specified by:
    
    createAlgorithm in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createApp
```
public CreateAppResult createApp(CreateAppRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateApp operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createAppImageConfig
```
public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a configuration for running an Amazon SageMaker image as a KernelGateway app.
    
    Specified by:
    
    createAppImageConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateAppImageConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createAutoMLJob
```
public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates an Autopilot job.
    
    Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
    
    For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
    
    Specified by:
    
    createAutoMLJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateAutoMLJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createCodeRepository
```
public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 AWS CodeCommit or in any other Git repository.
    
    Specified by:
    
    createCodeRepository in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createCompilationJob
```
public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    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 AWS 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
    
    Returns:
    
    Result of the CreateCompilationJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createDomain
```
public CreateDomainResult createDomain(CreateDomainRequest request)
```
    Description copied from interface: AmazonSageMaker
    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 AWS account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
    
    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.
    
    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.
    For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
    Specified by:
    
    createDomain in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateDomain operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createEndpoint
```
public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
    
    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 AWS Region in your AWS 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 AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
    
    Specified by:
    
    createEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createEndpointConfig
```
public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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.
    
    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 (AWS SDK for Python (Boto 3)).
    
    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
    
    Returns:
    
    Result of the CreateEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createExperiment
```
public CreateExperimentResult createExperiment(CreateExperimentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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
    
    Returns:
    
    Result of the CreateExperiment operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createFlowDefinition
```
public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a flow definition.
    
    Specified by:
    
    createFlowDefinition in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateFlowDefinition operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createHumanTaskUi
```
public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateHumanTaskUi operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createHyperParameterTuningJob
```
public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createImage
```
public CreateImageResult createImage(CreateImageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a SageMaker Image. A SageMaker image represents a set of container images. Each of these container images is represented by a SageMaker ImageVersion.
    
    Specified by:
    
    createImage in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateImage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createImageVersion
```
public CreateImageVersionResult createImageVersion(CreateImageVersionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateImageVersion operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createLabelingJob
```
public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    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 AWS 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.
    Specified by:
    
    createLabelingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateLabelingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createModel
```
public CreateModelResult createModel(CreateModelRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 (AWS 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 AWS resources, you grant necessary permissions via this role.
    
    Specified by:
    
    createModel in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createModelPackage
```
public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS 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 AWS Marketplace, provide a value for SourceAlgorithmSpecification.
    
    Specified by:
    
    createModelPackage in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createMonitoringSchedule
```
public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstance
```
public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    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
    
    Returns:
    
    Result of the CreateNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstanceLifecycleConfig
```
public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createPresignedDomainUrl
```
public CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 URL that you get from a call to CreatePresignedDomainUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
    
    Specified by:
    
    createPresignedDomainUrl in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreatePresignedDomainUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createPresignedNotebookInstanceUrl
```
public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 AWS console sign-in page.
    
    Specified by:
    
    createPresignedNotebookInstanceUrl in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createProcessingJob
```
public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a processing job.
    
    Specified by:
    
    createProcessingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateProcessingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTrainingJob
```
public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    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 inferences.
    
    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 Number (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 you are willing to wait for a managed spot training job to complete.
    For more information about Amazon SageMaker, see How It Works.
    Specified by:
    
    createTrainingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTransformJob
```
public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    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 AWS Region in an AWS account.
    - ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS 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
    
    Returns:
    
    Result of the CreateTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTrial
```
public CreateTrialResult createTrial(CreateTrialRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates an Amazon 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 Amazon SageMaker experiment.
    
    When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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
    
    Returns:
    
    Result of the CreateTrial operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTrialComponent
```
public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
    
    CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call to CreateTrialComponent from outside one of these environments results in an error.
    
    Specified by:
    
    createTrialComponent in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createUserProfile
```
public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateUserProfile operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createWorkforce
```
public CreateWorkforceResult createWorkforce(CreateWorkforceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
    
    If you want to create a new workforce in an AWS 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
    
    Returns:
    
    Result of the CreateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createWorkteam
```
public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the CreateWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteAlgorithm
```
public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Removes the specified algorithm from your account.
    
    Specified by:
    
    deleteAlgorithm in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteApp
```
public DeleteAppResult deleteApp(DeleteAppRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Used to stop and delete an app.
    
    Specified by:
    
    deleteApp in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteApp operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteAppImageConfig
```
public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes an AppImageConfig.
    
    Specified by:
    
    deleteAppImageConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteAppImageConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteCodeRepository
```
public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes the specified Git repository from your account.
    
    Specified by:
    
    deleteCodeRepository in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteDomain
```
public DeleteDomainResult deleteDomain(DeleteDomainRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteDomain operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpoint
```
public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpointConfig
```
public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteExperiment
```
public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes an Amazon 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
    
    Returns:
    
    Result of the DeleteExperiment operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteFlowDefinition
```
public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes the specified flow definition.
    
    Specified by:
    
    deleteFlowDefinition in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteFlowDefinition operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteHumanTaskUi
```
public DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteHumanTaskUi operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteImage
```
public DeleteImageResult deleteImage(DeleteImageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
    
    Specified by:
    
    deleteImage in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteImage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteImageVersion
```
public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
    
    Specified by:
    
    deleteImageVersion in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteImageVersion operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModel
```
public DeleteModelResult deleteModel(DeleteModelRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackage
```
public DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes a model package.
    
    A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
    
    Specified by:
    
    deleteModelPackage in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteMonitoringSchedule
```
public DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstance
```
public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstanceLifecycleConfig
```
public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes a notebook instance lifecycle configuration.
    
    Specified by:
    
    deleteNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTags
```
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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.
    
    Specified by:
    
    deleteTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTrial
```
public DeleteTrialResult deleteTrial(DeleteTrialRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteTrial operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTrialComponent
```
public DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteUserProfile
```
public DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DeleteUserProfile operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkforce
```
public DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Use this operation to delete a workforce.
    
    If you want to create a new workforce in an AWS 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
    
    Returns:
    
    Result of the DeleteWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkteam
```
public DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes an existing work team. This operation can't be undone.
    
    Specified by:
    
    deleteWorkteam in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeAlgorithm
```
public DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a description of the specified algorithm that is in your account.
    
    Specified by:
    
    describeAlgorithm in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeApp
```
public DescribeAppResult describeApp(DescribeAppRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes the app.
    
    Specified by:
    
    describeApp in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeApp operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeAppImageConfig
```
public DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes an AppImageConfig.
    
    Specified by:
    
    describeAppImageConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeAppImageConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeAutoMLJob
```
public DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about an Amazon SageMaker job.
    
    Specified by:
    
    describeAutoMLJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeAutoMLJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeCodeRepository
```
public DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets details about the specified Git repository.
    
    Specified by:
    
    describeCodeRepository in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeCompilationJob
```
public DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DescribeCompilationJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeDomain
```
public DescribeDomainResult describeDomain(DescribeDomainRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    The description of the domain.
    
    Specified by:
    
    describeDomain in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeDomain operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpoint
```
public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the description of an endpoint.
    
    Specified by:
    
    describeEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpointConfig
```
public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
    
    Specified by:
    
    describeEndpointConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeExperiment
```
public DescribeExperimentResult describeExperiment(DescribeExperimentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Provides a list of an experiment's properties.
    
    Specified by:
    
    describeExperiment in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeExperiment operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeFlowDefinition
```
public DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about the specified flow definition.
    
    Specified by:
    
    describeFlowDefinition in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeFlowDefinition operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeHumanTaskUi
```
public DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about the requested human task user interface (worker task template).
    
    Specified by:
    
    describeHumanTaskUi in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeHumanTaskUi operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeHyperParameterTuningJob
```
public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a description of a hyperparameter tuning job.
    
    Specified by:
    
    describeHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeImage
```
public DescribeImageResult describeImage(DescribeImageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes a SageMaker image.
    
    Specified by:
    
    describeImage in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeImage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeImageVersion
```
public DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes a version of a SageMaker image.
    
    Specified by:
    
    describeImageVersion in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeImageVersion operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeLabelingJob
```
public DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets information about a labeling job.
    
    Specified by:
    
    describeLabelingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeLabelingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModel
```
public DescribeModelResult describeModel(DescribeModelRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes a model that you created using the CreateModel API.
    
    Specified by:
    
    describeModel in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackage
```
public DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
    
    To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
    
    Specified by:
    
    describeModelPackage in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeMonitoringSchedule
```
public DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes the schedule for a monitoring job.
    
    Specified by:
    
    describeMonitoringSchedule in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstance
```
public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a notebook instance.
    
    Specified by:
    
    describeNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstanceLifecycleConfig
```
public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeProcessingJob
```
public DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a description of a processing job.
    
    Specified by:
    
    describeProcessingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeProcessingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeSubscribedWorkteam
```
public DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
    
    Specified by:
    
    describeSubscribedWorkteam in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeSubscribedWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrainingJob
```
public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a training job.
    
    Specified by:
    
    describeTrainingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTransformJob
```
public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a transform job.
    
    Specified by:
    
    describeTransformJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrial
```
public DescribeTrialResult describeTrial(DescribeTrialRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Provides a list of a trial's properties.
    
    Specified by:
    
    describeTrial in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTrial operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrialComponent
```
public DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Provides a list of a trials component's properties.
    
    Specified by:
    
    describeTrialComponent in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeUserProfile
```
public DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes a user profile. For more information, see CreateUserProfile.
    
    Specified by:
    
    describeUserProfile in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeUserProfile operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeWorkforce
```
public DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DescribeWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeWorkteam
```
public DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DescribeWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disassociateTrialComponent
```
public DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the DisassociateTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getSearchSuggestions
```
public GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the GetSearchSuggestions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAlgorithms
```
public ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists the machine learning algorithms that have been created.
    
    Specified by:
    
    listAlgorithms in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListAlgorithms operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAppImageConfigs
```
public ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListAppImageConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listApps
```
public ListAppsResult listApps(ListAppsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists apps.
    
    Specified by:
    
    listApps in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListApps operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAutoMLJobs
```
public ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Request a list of jobs.
    
    Specified by:
    
    listAutoMLJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListAutoMLJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCandidatesForAutoMLJob
```
public ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    List the Candidates created for the job.
    
    Specified by:
    
    listCandidatesForAutoMLJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListCandidatesForAutoMLJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCodeRepositories
```
public ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of the Git repositories in your account.
    
    Specified by:
    
    listCodeRepositories in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListCodeRepositories operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCompilationJobs
```
public ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListCompilationJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDomains
```
public ListDomainsResult listDomains(ListDomainsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists the domains.
    
    Specified by:
    
    listDomains in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListDomains operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpointConfigs
```
public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists endpoint configurations.
    
    Specified by:
    
    listEndpointConfigs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListEndpointConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpoints
```
public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists endpoints.
    
    Specified by:
    
    listEndpoints in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListEndpoints operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listExperiments
```
public ListExperimentsResult listExperiments(ListExperimentsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListExperiments operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFlowDefinitions
```
public ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about the flow definitions in your account.
    
    Specified by:
    
    listFlowDefinitions in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListFlowDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHumanTaskUis
```
public ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about the human task user interfaces in your account.
    
    Specified by:
    
    listHumanTaskUis in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListHumanTaskUis operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHyperParameterTuningJobs
```
public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
    
    Specified by:
    
    listHyperParameterTuningJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListHyperParameterTuningJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listImageVersions
```
public ListImageVersionsResult listImageVersions(ListImageVersionsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListImageVersions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listImages
```
public ListImagesResult listImages(ListImagesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListImages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobs
```
public ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of labeling jobs.
    
    Specified by:
    
    listLabelingJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListLabelingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobsForWorkteam
```
public ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of labeling jobs assigned to a specified work team.
    
    Specified by:
    
    listLabelingJobsForWorkteam in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListLabelingJobsForWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelPackages
```
public ListModelPackagesResult listModelPackages(ListModelPackagesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists the model packages that have been created.
    
    Specified by:
    
    listModelPackages in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListModelPackages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModels
```
public ListModelsResult listModels(ListModelsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists models created with the CreateModel API.
    
    Specified by:
    
    listModels in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListModels operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringExecutions
```
public ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns list of all monitoring job executions.
    
    Specified by:
    
    listMonitoringExecutions in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListMonitoringExecutions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringSchedules
```
public ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns list of all monitoring schedules.
    
    Specified by:
    
    listMonitoringSchedules in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListMonitoringSchedules operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstanceLifecycleConfigs
```
public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    listNotebookInstanceLifecycleConfigs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstances
```
public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
    
    Specified by:
    
    listNotebookInstances in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListNotebookInstances operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProcessingJobs
```
public ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists processing jobs that satisfy various filters.
    
    Specified by:
    
    listProcessingJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListProcessingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listSubscribedWorkteams
```
public ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Specified by:
    
    listSubscribedWorkteams in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListSubscribedWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTags
```
public ListTagsResult listTags(ListTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the tags for the specified Amazon SageMaker resource.
    
    Specified by:
    
    listTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobs
```
public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists training jobs.
    
    Specified by:
    
    listTrainingJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTrainingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobsForHyperParameterTuningJob
```
public ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
    
    Specified by:
    
    listTrainingJobsForHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTransformJobs
```
public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists transform jobs.
    
    Specified by:
    
    listTransformJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTransformJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrialComponents
```
public ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest request)
```
    Description copied from interface: AmazonSageMaker
    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
    
    Returns:
    
    Result of the ListTrialComponents operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrials
```
public ListTrialsResult listTrials(ListTrialsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListTrials operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listUserProfiles
```
public ListUserProfilesResult listUserProfiles(ListUserProfilesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists user profiles.
    
    Specified by:
    
    listUserProfiles in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListUserProfiles operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkforces
```
public ListWorkforcesResult listWorkforces(ListWorkforcesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Use this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region.
    
    Specified by:
    
    listWorkforces in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListWorkforces operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkteams
```
public ListWorkteamsResult listWorkteams(ListWorkteamsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the ListWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - renderUiTemplate
```
public RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Renders the UI template so that you can preview the worker's experience.
    
    Specified by:
    
    renderUiTemplate in interface AmazonSageMaker
    
    Returns:
    
    Result of the RenderUiTemplate operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - search
```
public SearchResult search(SearchRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the Search operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - startMonitoringSchedule
```
public StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Starts a previously stopped monitoring schedule.
    
    New monitoring schedules are immediately started after creation.
    
    Specified by:
    
    startMonitoringSchedule in interface AmazonSageMaker
    
    Returns:
    
    Result of the StartMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - startNotebookInstance
```
public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StartNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopAutoMLJob
```
public StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    A method for forcing the termination of a running job.
    
    Specified by:
    
    stopAutoMLJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopAutoMLJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopCompilationJob
```
public StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StopCompilationJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopHyperParameterTuningJob
```
public StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StopHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopLabelingJob
```
public StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StopLabelingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopMonitoringSchedule
```
public StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Stops a previously started monitoring schedule.
    
    Specified by:
    
    stopMonitoringSchedule in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopNotebookInstance
```
public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StopNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopProcessingJob
```
public StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Stops a processing job.
    
    Specified by:
    
    stopProcessingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopProcessingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopTrainingJob
```
public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the StopTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopTransformJob
```
public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Stops a 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 transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
    
    Specified by:
    
    stopTransformJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateAppImageConfig
```
public UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates the properties of an AppImageConfig.
    
    Specified by:
    
    updateAppImageConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateAppImageConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateCodeRepository
```
public UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates the specified Git repository with the specified values.
    
    Specified by:
    
    updateCodeRepository in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateDomain
```
public UpdateDomainResult updateDomain(UpdateDomainRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates the default settings for new user profiles in the domain.
    
    Specified by:
    
    updateDomain in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateDomain operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateEndpoint
```
public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the UpdateEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateEndpointWeightsAndCapacities
```
public UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the UpdateEndpointWeightsAndCapacities operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateExperiment
```
public UpdateExperimentResult updateExperiment(UpdateExperimentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
    
    Specified by:
    
    updateExperiment in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateExperiment operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateImage
```
public UpdateImageResult updateImage(UpdateImageRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the UpdateImage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateMonitoringSchedule
```
public UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates a previously created schedule.
    
    Specified by:
    
    updateMonitoringSchedule in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateMonitoringSchedule operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstance
```
public UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the UpdateNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstanceLifecycleConfig
```
public UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    updateNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateTrial
```
public UpdateTrialResult updateTrial(UpdateTrialRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates the display name of a trial.
    
    Specified by:
    
    updateTrial in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateTrial operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateTrialComponent
```
public UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates one or more properties of a trial component.
    
    Specified by:
    
    updateTrialComponent in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateTrialComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateUserProfile
```
public UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates a user profile.
    
    Specified by:
    
    updateUserProfile in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateUserProfile operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateWorkforce
```
public UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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
    
    Returns:
    
    Result of the UpdateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateWorkteam
```
public UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates an existing work team with new member definitions or description.
    
    Specified by:
    
    updateWorkteam in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - shutdown
```
public void shutdown()
```
    Description copied from interface: AmazonSageMaker
    
    Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
    
    Specified by:
    
    shutdown in interface AmazonSageMaker
  - getCachedResponseMetadata
```
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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 a 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

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Class AbstractAmazonSageMaker

Field Summary

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

Method Summary

Methods inherited from class java.lang.Object

Method Detail

addTags

associateTrialComponent

createAlgorithm

createApp

createAppImageConfig

createAutoMLJob

createCodeRepository

createCompilationJob

createDomain

createEndpoint

createEndpointConfig

createExperiment

createFlowDefinition

createHumanTaskUi

createHyperParameterTuningJob

createImage

createImageVersion

createLabelingJob

createModel

createModelPackage

createMonitoringSchedule

createNotebookInstance

createNotebookInstanceLifecycleConfig

createPresignedDomainUrl

createPresignedNotebookInstanceUrl

createProcessingJob

createTrainingJob

createTransformJob

createTrial

createTrialComponent

createUserProfile

createWorkforce

createWorkteam

deleteAlgorithm

deleteApp

deleteAppImageConfig

deleteCodeRepository

deleteDomain

deleteEndpoint

deleteEndpointConfig

deleteExperiment

deleteFlowDefinition

deleteHumanTaskUi

deleteImage

deleteImageVersion

deleteModel

deleteModelPackage

deleteMonitoringSchedule

deleteNotebookInstance

deleteNotebookInstanceLifecycleConfig

deleteTags

deleteTrial

deleteTrialComponent

deleteUserProfile

deleteWorkforce

deleteWorkteam

describeAlgorithm

describeApp

describeAppImageConfig

describeAutoMLJob

describeCodeRepository

describeCompilationJob

describeDomain

describeEndpoint

describeEndpointConfig

describeExperiment

describeFlowDefinition

describeHumanTaskUi

describeHyperParameterTuningJob

describeImage

describeImageVersion

describeLabelingJob

describeModel