public class AbstractAmazonMachineLearningAsync extends AbstractAmazonMachineLearning implements AmazonMachineLearningAsync
AmazonMachineLearningAsync. Convenient
method forms pass through to the corresponding overload that takes a request
object and an AsyncHandler, which throws an
UnsupportedOperationException.ENDPOINT_PREFIXaddTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModelequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitaddTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModelpublic Future<AddTagsResult> addTagsAsync(AddTagsRequest request)
AmazonMachineLearningAsync
Adds one or more tags to an object, up to a limit of 10. Each tag
consists of a key and an optional value. If you add a tag using a key
that is already associated with the ML object, AddTags
updates the tag's value.
addTagsAsync in interface AmazonMachineLearningAsyncpublic Future<AddTagsResult> addTagsAsync(AddTagsRequest request, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
AmazonMachineLearningAsync
Adds one or more tags to an object, up to a limit of 10. Each tag
consists of a key and an optional value. If you add a tag using a key
that is already associated with the ML object, AddTags
updates the tag's value.
addTagsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest request)
AmazonMachineLearningAsync
Generates predictions for a group of observations. The observations to
process exist in one or more data files referenced by a
DataSource. This operation creates a new
BatchPrediction, and uses an MLModel and the
data files referenced by the DataSource as information
sources.
CreateBatchPrediction is an asynchronous operation. In
response to CreateBatchPrediction, Amazon Machine Learning
(Amazon ML) immediately returns and sets the BatchPrediction
status to PENDING. After the BatchPrediction
completes, Amazon ML sets the status to COMPLETED.
You can poll for status updates by using the GetBatchPrediction
operation and checking the Status parameter of the result.
After the COMPLETED status appears, the results are
available in the location specified by the OutputUri
parameter.
createBatchPredictionAsync in interface AmazonMachineLearningAsyncpublic Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest request, AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Generates predictions for a group of observations. The observations to
process exist in one or more data files referenced by a
DataSource. This operation creates a new
BatchPrediction, and uses an MLModel and the
data files referenced by the DataSource as information
sources.
CreateBatchPrediction is an asynchronous operation. In
response to CreateBatchPrediction, Amazon Machine Learning
(Amazon ML) immediately returns and sets the BatchPrediction
status to PENDING. After the BatchPrediction
completes, Amazon ML sets the status to COMPLETED.
You can poll for status updates by using the GetBatchPrediction
operation and checking the Status parameter of the result.
After the COMPLETED status appears, the results are
available in the location specified by the OutputUri
parameter.
createBatchPredictionAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request)
AmazonMachineLearningAsync
Creates a DataSource object from an Amazon Relational Database Service
(Amazon RDS). A DataSource references data that can be used
to perform CreateMLModel, CreateEvaluation, or
CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In
response to CreateDataSourceFromRDS, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used only to
perform >CreateMLModel>, CreateEvaluation
, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
createDataSourceFromRDSAsync in interface AmazonMachineLearningAsyncpublic Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource object from an Amazon Relational Database Service
(Amazon RDS). A DataSource references data that can be used
to perform CreateMLModel, CreateEvaluation, or
CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In
response to CreateDataSourceFromRDS, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used only to
perform >CreateMLModel>, CreateEvaluation
, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
createDataSourceFromRDSAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request)
AmazonMachineLearningAsync
Creates a DataSource from a database hosted on an Amazon
Redshift cluster. A DataSource references data that can be
used to perform either CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromRedshift is an asynchronous operation.
In response to CreateDataSourceFromRedshift, Amazon Machine
Learning (Amazon ML) immediately returns and sets the
DataSource status to PENDING. After the
DataSource is created and ready for use, Amazon ML sets the
Status parameter to COMPLETED.
DataSource in COMPLETED or PENDING
states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
query. Amazon ML executes an Unload command in Amazon
Redshift to transfer the result set of the SelectSqlQuery
query to S3StagingLocation.
After the DataSource has been created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also requires a recipe. A recipe describes how
each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the
variable be manipulated; for example, will it be combined with another
variable or will it be split apart into word combinations? The recipe
provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the
settings from an existing Amazon Redshift datasource to create a new
datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call.
Change the settings that you want to change and make sure that all
required fields have the appropriate values.
createDataSourceFromRedshiftAsync in interface AmazonMachineLearningAsyncpublic Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource from a database hosted on an Amazon
Redshift cluster. A DataSource references data that can be
used to perform either CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromRedshift is an asynchronous operation.
In response to CreateDataSourceFromRedshift, Amazon Machine
Learning (Amazon ML) immediately returns and sets the
DataSource status to PENDING. After the
DataSource is created and ready for use, Amazon ML sets the
Status parameter to COMPLETED.
DataSource in COMPLETED or PENDING
states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
query. Amazon ML executes an Unload command in Amazon
Redshift to transfer the result set of the SelectSqlQuery
query to S3StagingLocation.
After the DataSource has been created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also requires a recipe. A recipe describes how
each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the
variable be manipulated; for example, will it be combined with another
variable or will it be split apart into word combinations? The recipe
provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the
settings from an existing Amazon Redshift datasource to create a new
datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call.
Change the settings that you want to change and make sure that all
required fields have the appropriate values.
createDataSourceFromRedshiftAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request)
AmazonMachineLearningAsync
Creates a DataSource object. A DataSource
references data that can be used to perform CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromS3 is an asynchronous operation. In
response to CreateDataSourceFromS3, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource has
been created and is ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used to
perform only CreateMLModel, CreateEvaluation or
CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observation data used in a DataSource should be ready to
use; that is, it should have a consistent structure, and missing data
values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3)
location, along with a schema that describes the data items by name and
type. The same schema must be used for all of the data files referenced
by the DataSource.
After the DataSource has been created, it's ready to use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also needs a recipe. A recipe describes how each
input variable will be used in training an MLModel. Will the
variable be included or excluded from training? Will the variable be
manipulated; for example, will it be combined with another variable or
will it be split apart into word combinations? The recipe provides
answers to these questions.
createDataSourceFromS3Async in interface AmazonMachineLearningAsyncpublic Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource object. A DataSource
references data that can be used to perform CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromS3 is an asynchronous operation. In
response to CreateDataSourceFromS3, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource has
been created and is ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used to
perform only CreateMLModel, CreateEvaluation or
CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observation data used in a DataSource should be ready to
use; that is, it should have a consistent structure, and missing data
values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3)
location, along with a schema that describes the data items by name and
type. The same schema must be used for all of the data files referenced
by the DataSource.
After the DataSource has been created, it's ready to use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also needs a recipe. A recipe describes how each
input variable will be used in training an MLModel. Will the
variable be included or excluded from training? Will the variable be
manipulated; for example, will it be combined with another variable or
will it be split apart into word combinations? The recipe provides
answers to these questions.
createDataSourceFromS3Async in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest request)
AmazonMachineLearningAsync
Creates a new Evaluation of an MLModel. An
MLModel is evaluated on a set of observations associated to
a DataSource. Like a DataSource for an
MLModel, the DataSource for an
Evaluation contains values for the
Target Variable. The Evaluation compares the
predicted result for each observation to the actual outcome and provides
a summary so that you know how effective the MLModel
functions on the test data. Evaluation generates a relevant performance
metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on
the corresponding MLModelType: BINARY,
REGRESSION or MULTICLASS.
CreateEvaluation is an asynchronous operation. In response
to CreateEvaluation, Amazon Machine Learning (Amazon ML)
immediately returns and sets the evaluation status to
PENDING. After the Evaluation is created and
ready for use, Amazon ML sets the status to COMPLETED.
You can use the GetEvaluation operation to check progress of
the evaluation during the creation operation.
createEvaluationAsync in interface AmazonMachineLearningAsyncpublic Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest request, AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Creates a new Evaluation of an MLModel. An
MLModel is evaluated on a set of observations associated to
a DataSource. Like a DataSource for an
MLModel, the DataSource for an
Evaluation contains values for the
Target Variable. The Evaluation compares the
predicted result for each observation to the actual outcome and provides
a summary so that you know how effective the MLModel
functions on the test data. Evaluation generates a relevant performance
metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on
the corresponding MLModelType: BINARY,
REGRESSION or MULTICLASS.
CreateEvaluation is an asynchronous operation. In response
to CreateEvaluation, Amazon Machine Learning (Amazon ML)
immediately returns and sets the evaluation status to
PENDING. After the Evaluation is created and
ready for use, Amazon ML sets the status to COMPLETED.
You can use the GetEvaluation operation to check progress of
the evaluation during the creation operation.
createEvaluationAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request)
AmazonMachineLearningAsync
Creates a new MLModel using the DataSource and
the recipe as information sources.
An MLModel is nearly immutable. Users can update only the
MLModelName and the ScoreThreshold in an
MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to
CreateMLModel, Amazon Machine Learning (Amazon ML)
immediately returns and sets the MLModel status to
PENDING. After the MLModel has been created and
ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress
of the MLModel during the creation operation.
CreateMLModel requires a DataSource with
computed statistics, which can be created by setting
ComputeStatistics to true in
CreateDataSourceFromRDS, CreateDataSourceFromS3
, or CreateDataSourceFromRedshift operations.
createMLModelAsync in interface AmazonMachineLearningAsyncpublic Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Creates a new MLModel using the DataSource and
the recipe as information sources.
An MLModel is nearly immutable. Users can update only the
MLModelName and the ScoreThreshold in an
MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to
CreateMLModel, Amazon Machine Learning (Amazon ML)
immediately returns and sets the MLModel status to
PENDING. After the MLModel has been created and
ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress
of the MLModel during the creation operation.
CreateMLModel requires a DataSource with
computed statistics, which can be created by setting
ComputeStatistics to true in
CreateDataSourceFromRDS, CreateDataSourceFromS3
, or CreateDataSourceFromRedshift operations.
createMLModelAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request)
AmazonMachineLearningAsync
Creates a real-time endpoint for the MLModel. The endpoint
contains the URI of the MLModel; that is, the location to
send real-time prediction requests for the specified MLModel
.
createRealtimeEndpointAsync in interface AmazonMachineLearningAsyncpublic Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request, AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
Creates a real-time endpoint for the MLModel. The endpoint
contains the URI of the MLModel; that is, the location to
send real-time prediction requests for the specified MLModel
.
createRealtimeEndpointAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest request)
AmazonMachineLearningAsync
Assigns the DELETED status to a BatchPrediction, rendering
it unusable.
After using the DeleteBatchPrediction operation, you can use
the GetBatchPrediction operation to verify that the status of the
BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
deleteBatchPredictionAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest request, AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Assigns the DELETED status to a BatchPrediction, rendering
it unusable.
After using the DeleteBatchPrediction operation, you can use
the GetBatchPrediction operation to verify that the status of the
BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
deleteBatchPredictionAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest request)
AmazonMachineLearningAsync
Assigns the DELETED status to a DataSource, rendering it
unusable.
After using the DeleteDataSource operation, you can use the
GetDataSource operation to verify that the status of the
DataSource changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteDataSourceAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest request, AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
Assigns the DELETED status to a DataSource, rendering it
unusable.
After using the DeleteDataSource operation, you can use the
GetDataSource operation to verify that the status of the
DataSource changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteDataSourceAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest request)
AmazonMachineLearningAsync
Assigns the DELETED status to an Evaluation,
rendering it unusable.
After invoking the DeleteEvaluation operation, you can use
the GetEvaluation operation to verify that the status of the
Evaluation changed to DELETED.
The results of the DeleteEvaluation operation are
irreversible.
deleteEvaluationAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest request, AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Assigns the DELETED status to an Evaluation,
rendering it unusable.
After invoking the DeleteEvaluation operation, you can use
the GetEvaluation operation to verify that the status of the
Evaluation changed to DELETED.
The results of the DeleteEvaluation operation are
irreversible.
deleteEvaluationAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest request)
AmazonMachineLearningAsync
Assigns the DELETED status to an MLModel,
rendering it unusable.
After using the DeleteMLModel operation, you can use the
GetMLModel operation to verify that the status of the
MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is
irreversible.
deleteMLModelAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest request, AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Assigns the DELETED status to an MLModel,
rendering it unusable.
After using the DeleteMLModel operation, you can use the
GetMLModel operation to verify that the status of the
MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is
irreversible.
deleteMLModelAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request)
AmazonMachineLearningAsyncDeletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsAsync in interface AmazonMachineLearningAsyncpublic Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
AmazonMachineLearningAsyncDeletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request)
AmazonMachineLearningAsync
Returns a list of BatchPrediction operations that match the
search criteria in the request.
describeBatchPredictionsAsync in interface AmazonMachineLearningAsyncpublic Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request, AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of BatchPrediction operations that match the
search criteria in the request.
describeBatchPredictionsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
describeBatchPredictionsAsync in interface AmazonMachineLearningAsyncdescribeBatchPredictionsAsync(DescribeBatchPredictionsRequest)public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
describeBatchPredictionsAsync in interface AmazonMachineLearningAsyncdescribeBatchPredictionsAsync(DescribeBatchPredictionsRequest,
com.amazonaws.handlers.AsyncHandler)public Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request)
AmazonMachineLearningAsync
Returns a list of DataSource that match the search criteria
in the request.
describeDataSourcesAsync in interface AmazonMachineLearningAsyncpublic Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request, AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of DataSource that match the search criteria
in the request.
describeDataSourcesAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeDataSourcesResult> describeDataSourcesAsync()
describeDataSourcesAsync in interface AmazonMachineLearningAsyncdescribeDataSourcesAsync(DescribeDataSourcesRequest)public Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
describeDataSourcesAsync in interface AmazonMachineLearningAsyncdescribeDataSourcesAsync(DescribeDataSourcesRequest,
com.amazonaws.handlers.AsyncHandler)public Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest request)
AmazonMachineLearningAsync
Returns a list of DescribeEvaluations that match the search
criteria in the request.
describeEvaluationsAsync in interface AmazonMachineLearningAsyncpublic Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest request, AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of DescribeEvaluations that match the search
criteria in the request.
describeEvaluationsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeEvaluationsResult> describeEvaluationsAsync()
describeEvaluationsAsync in interface AmazonMachineLearningAsyncdescribeEvaluationsAsync(DescribeEvaluationsRequest)public Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
describeEvaluationsAsync in interface AmazonMachineLearningAsyncdescribeEvaluationsAsync(DescribeEvaluationsRequest,
com.amazonaws.handlers.AsyncHandler)public Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest request)
AmazonMachineLearningAsync
Returns a list of MLModel that match the search criteria in
the request.
describeMLModelsAsync in interface AmazonMachineLearningAsyncpublic Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest request, AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of MLModel that match the search criteria in
the request.
describeMLModelsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeMLModelsResult> describeMLModelsAsync()
describeMLModelsAsync in interface AmazonMachineLearningAsyncdescribeMLModelsAsync(DescribeMLModelsRequest)public Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
describeMLModelsAsync in interface AmazonMachineLearningAsyncdescribeMLModelsAsync(DescribeMLModelsRequest,
com.amazonaws.handlers.AsyncHandler)public Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request)
AmazonMachineLearningAsyncDescribes one or more of the tags for your Amazon ML object.
describeTagsAsync in interface AmazonMachineLearningAsyncpublic Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request, AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
AmazonMachineLearningAsyncDescribes one or more of the tags for your Amazon ML object.
describeTagsAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest request)
AmazonMachineLearningAsync
Returns a BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPredictionAsync in interface AmazonMachineLearningAsyncpublic Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest request, AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Returns a BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPredictionAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest request)
AmazonMachineLearningAsync
Returns a DataSource that includes metadata and data file
information, as well as the current status of the DataSource
.
GetDataSource provides results in normal or verbose format.
The verbose format adds the schema description and the list of files
pointed to by the DataSource to the normal format.
getDataSourceAsync in interface AmazonMachineLearningAsyncpublic Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest request, AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
Returns a DataSource that includes metadata and data file
information, as well as the current status of the DataSource
.
GetDataSource provides results in normal or verbose format.
The verbose format adds the schema description and the list of files
pointed to by the DataSource to the normal format.
getDataSourceAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest request)
AmazonMachineLearningAsync
Returns an Evaluation that includes metadata as well as the
current status of the Evaluation.
getEvaluationAsync in interface AmazonMachineLearningAsyncpublic Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest request, AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Returns an Evaluation that includes metadata as well as the
current status of the Evaluation.
getEvaluationAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request)
AmazonMachineLearningAsync
Returns an MLModel that includes detailed metadata, data
source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelAsync in interface AmazonMachineLearningAsyncpublic Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Returns an MLModel that includes detailed metadata, data
source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<PredictResult> predictAsync(PredictRequest request)
AmazonMachineLearningAsync
Generates a prediction for the observation using the specified
ML Model.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictAsync in interface AmazonMachineLearningAsyncpublic Future<PredictResult> predictAsync(PredictRequest request, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
AmazonMachineLearningAsync
Generates a prediction for the observation using the specified
ML Model.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest request)
AmazonMachineLearningAsync
Updates the BatchPredictionName of a
BatchPrediction.
You can use the GetBatchPrediction operation to view the
contents of the updated data element.
updateBatchPredictionAsync in interface AmazonMachineLearningAsyncpublic Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest request, AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Updates the BatchPredictionName of a
BatchPrediction.
You can use the GetBatchPrediction operation to view the
contents of the updated data element.
updateBatchPredictionAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest request)
AmazonMachineLearningAsync
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents
of the updated data element.
updateDataSourceAsync in interface AmazonMachineLearningAsyncpublic Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest request, AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents
of the updated data element.
updateDataSourceAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest request)
AmazonMachineLearningAsync
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents
of the updated data element.
updateEvaluationAsync in interface AmazonMachineLearningAsyncpublic Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest request, AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents
of the updated data element.
updateEvaluationAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest request)
AmazonMachineLearningAsync
Updates the MLModelName and the ScoreThreshold
of an MLModel.
You can use the GetMLModel operation to view the contents of
the updated data element.
updateMLModelAsync in interface AmazonMachineLearningAsyncpublic Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest request, AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Updates the MLModelName and the ScoreThreshold
of an MLModel.
You can use the GetMLModel operation to view the contents of
the updated data element.
updateMLModelAsync in interface AmazonMachineLearningAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.