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.| Modifier | Constructor and Description |
|---|---|
protected |
AbstractAmazonMachineLearningAsync() |
createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModelclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreateBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModelprotected AbstractAmazonMachineLearningAsync()
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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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
COMPLETED or PENDING status can only be used 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 AmazonMachineLearningAsyncrequest - nullpublic 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
COMPLETED or PENDING status can only be used 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 Amazon Redshift. 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
status can only be used 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.
The observations should exist in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
. Amazon ML executes
Unload command in Amazon Redshift to transfer the result set of
SelectSqlQuery to S3StagingLocation.
After the DataSource is created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource requires another item -- a recipe. A recipe
describes the observation variables that participate in training an
MLModel. A recipe describes how each input variable will be
used in training. Will the variable be included or excluded from
training? Will the variable be manipulated, for example, combined with
another variable or split apart into word combinations? The recipe
provides answers to these questions. For more information, see the Amazon
Machine Learning Developer Guide.
createDataSourceFromRedshiftAsync in interface AmazonMachineLearningAsyncrequest - nullpublic Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource from Amazon Redshift. 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
status can only be used 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.
The observations should exist in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
. Amazon ML executes
Unload command in Amazon Redshift to transfer the result set of
SelectSqlQuery to S3StagingLocation.
After the DataSource is created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource requires another item -- a recipe. A recipe
describes the observation variables that participate in training an
MLModel. A recipe describes how each input variable will be
used in training. Will the variable be included or excluded from
training? Will the variable be manipulated, for example, combined with
another variable or split apart into word combinations? The recipe
provides answers to these questions. For more information, see the Amazon
Machine Learning Developer Guide.
createDataSourceFromRedshiftAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in
COMPLETED or PENDING status can only be used 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.
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)
bucket, 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 requires another item: a recipe. A recipe
describes the observation variables that participate in training an
MLModel. A recipe describes how each input variable will be
used in training. Will the variable be included or excluded from
training? Will the variable be manipulated, for example, combined with
another variable, or split apart into word combinations? The recipe
provides answers to these questions. For more information, see the Amazon
Machine Learning Developer Guide.
createDataSourceFromS3Async in interface AmazonMachineLearningAsyncrequest - nullpublic 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 is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in
COMPLETED or PENDING status can only be used 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.
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)
bucket, 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 requires another item: a recipe. A recipe
describes the observation variables that participate in training an
MLModel. A recipe describes how each input variable will be
used in training. Will the variable be included or excluded from
training? Will the variable be manipulated, for example, combined with
another variable, or split apart into word combinations? The recipe
provides answers to these questions. For more information, see the Amazon
Machine Learning Developer Guide.
createDataSourceFromS3Async in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 data files and the recipe as
information sources.
An MLModel is nearly immutable. Users can only update 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 is created and ready
for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check 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 AmazonMachineLearningAsyncrequest - nullpublic Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Creates a new MLModel using the data files and the recipe as
information sources.
An MLModel is nearly immutable. Users can only update 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 is created and ready
for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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.
The result of the DeleteBatchPrediction operation is
irreversible.
deleteBatchPredictionAsync in interface AmazonMachineLearningAsyncrequest - nullpublic 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.
The result of the DeleteBatchPrediction operation is
irreversible.
deleteBatchPredictionAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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.
The results of the DeleteDataSource operation are
irreversible.
deleteDataSourceAsync in interface AmazonMachineLearningAsyncrequest - nullpublic 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.
The results of the DeleteDataSource operation are
irreversible.
deleteDataSourceAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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.
The result of the DeleteMLModel operation is irreversible.
deleteMLModelAsync in interface AmazonMachineLearningAsyncrequest - nullpublic 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.
The result of the DeleteMLModel operation is irreversible.
deleteMLModelAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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)
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request)
AmazonMachineLearningAsync
Returns a list of DataSource that match the search criteria
in the request.
describeDataSourcesAsync in interface AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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<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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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, and data
source information as well as the current status of the
MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelAsync in interface AmazonMachineLearningAsyncrequest - nullpublic Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Returns an MLModel that includes detailed metadata, and data
source information as well as the current status of the
MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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
MLModel.
Not all response parameters will be populated because this is dependent on the type of requested model.
predictAsync in interface AmazonMachineLearningAsyncrequest - nullpublic Future<PredictResult> predictAsync(PredictRequest request, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
AmazonMachineLearningAsync
Generates a prediction for the observation using the specified
MLModel.
Not all response parameters will be populated because this is dependent on the type of requested model.
predictAsync in interface AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 AmazonMachineLearningAsyncrequest - nullpublic 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 AmazonMachineLearningAsyncrequest - nullasyncHandler - 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 © 2015. All rights reserved.