@ThreadSafe public class AmazonMachineLearningAsyncClient extends AmazonMachineLearningClient implements AmazonMachineLearningAsync
AsyncHandler
can be
used to receive notification when an asynchronous operation completes.
Definition of the public APIs exposed by Amazon Machine Learning
Modifier and Type | Field and Description |
---|---|
private static int |
DEFAULT_THREAD_POOL_SIZE |
private java.util.concurrent.ExecutorService |
executorService |
configFactory
client, clientConfiguration, endpoint, LOGGING_AWS_REQUEST_METRIC, requestHandler2s, timeOffset
Constructor and Description |
---|
AmazonMachineLearningAsyncClient()
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning.
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials.
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials,
ClientConfiguration clientConfiguration,
java.util.concurrent.ExecutorService executorService)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials, executor
service, and client configuration options.
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials,
java.util.concurrent.ExecutorService executorService)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials and executor
service.
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials provider.
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the provided AWS account credentials provider and
client configuration options.
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration,
java.util.concurrent.ExecutorService executorService)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials provider,
executor service, and client configuration options.
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
java.util.concurrent.ExecutorService executorService)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning using the specified AWS account credentials provider and
executor service.
|
AmazonMachineLearningAsyncClient(ClientConfiguration clientConfiguration)
Constructs a new asynchronous client to invoke service methods on Amazon
Machine Learning.
|
Modifier and Type | Method and Description |
---|---|
java.util.concurrent.Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest request)
Generates predictions for a group of observations.
|
java.util.concurrent.Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest request,
AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
Generates predictions for a group of observations.
|
java.util.concurrent.Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request)
Creates a
DataSource object from an Amazon Relational Database Service
(Amazon RDS). |
java.util.concurrent.Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request,
AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
Creates a
DataSource object from an Amazon Relational Database Service
(Amazon RDS). |
java.util.concurrent.Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request)
Creates a
DataSource from Amazon Redshift. |
java.util.concurrent.Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request,
AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a
DataSource from Amazon Redshift. |
java.util.concurrent.Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request request)
Creates a
DataSource object. |
java.util.concurrent.Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request request,
AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
Creates a
DataSource object. |
java.util.concurrent.Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest request)
Creates a new
Evaluation of an MLModel . |
java.util.concurrent.Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest request,
AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
Creates a new
Evaluation of an MLModel . |
java.util.concurrent.Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest request)
Creates a new
MLModel using the data files and the recipe as
information sources. |
java.util.concurrent.Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest request,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the recipe as
information sources. |
java.util.concurrent.Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request)
Creates a real-time endpoint for the
MLModel . |
java.util.concurrent.Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request,
AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
Creates a real-time endpoint for the
MLModel . |
java.util.concurrent.Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest request)
Assigns the DELETED status to a
BatchPrediction , rendering
it unusable. |
java.util.concurrent.Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest request,
AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
Assigns the DELETED status to a
BatchPrediction , rendering
it unusable. |
java.util.concurrent.Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest request)
Assigns the DELETED status to a
DataSource , rendering it
unusable. |
java.util.concurrent.Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest request,
AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
Assigns the DELETED status to a
DataSource , rendering it
unusable. |
java.util.concurrent.Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest request)
Assigns the
DELETED status to an Evaluation ,
rendering it unusable. |
java.util.concurrent.Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest request,
AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
Assigns the
DELETED status to an Evaluation ,
rendering it unusable. |
java.util.concurrent.Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest request)
Assigns the DELETED status to an
MLModel , rendering it
unusable. |
java.util.concurrent.Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest request,
AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
Assigns the DELETED status to an
MLModel , rendering it
unusable. |
java.util.concurrent.Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request)
Deletes a real time endpoint of an
MLModel . |
java.util.concurrent.Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request,
AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
Deletes a real time endpoint of an
MLModel . |
java.util.concurrent.Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync()
Simplified method form for invoking the DescribeBatchPredictions
operation.
|
java.util.concurrent.Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Simplified method form for invoking the DescribeBatchPredictions
operation with an AsyncHandler.
|
java.util.concurrent.Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request)
Returns a list of
BatchPrediction operations that match the
search criteria in the request. |
java.util.concurrent.Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request,
AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Returns a list of
BatchPrediction operations that match the
search criteria in the request. |
java.util.concurrent.Future<DescribeDataSourcesResult> |
describeDataSourcesAsync()
Simplified method form for invoking the DescribeDataSources operation.
|
java.util.concurrent.Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Simplified method form for invoking the DescribeDataSources operation
with an AsyncHandler.
|
java.util.concurrent.Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest request)
Returns a list of
DataSource that match the search criteria
in the request. |
java.util.concurrent.Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest request,
AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Returns a list of
DataSource that match the search criteria
in the request. |
java.util.concurrent.Future<DescribeEvaluationsResult> |
describeEvaluationsAsync()
Simplified method form for invoking the DescribeEvaluations operation.
|
java.util.concurrent.Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Simplified method form for invoking the DescribeEvaluations operation
with an AsyncHandler.
|
java.util.concurrent.Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest request)
Returns a list of
DescribeEvaluations that match the search
criteria in the request. |
java.util.concurrent.Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest request,
AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Returns a list of
DescribeEvaluations that match the search
criteria in the request. |
java.util.concurrent.Future<DescribeMLModelsResult> |
describeMLModelsAsync()
Simplified method form for invoking the DescribeMLModels operation.
|
java.util.concurrent.Future<DescribeMLModelsResult> |
describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Simplified method form for invoking the DescribeMLModels operation with
an AsyncHandler.
|
java.util.concurrent.Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest request)
Returns a list of
MLModel that match the search criteria in
the request. |
java.util.concurrent.Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest request,
AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Returns a list of
MLModel that match the search criteria in
the request. |
java.util.concurrent.Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest request)
Returns a
BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request. |
java.util.concurrent.Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest request,
AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
Returns a
BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request. |
java.util.concurrent.Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest request)
Returns a
DataSource that includes metadata and data file
information, as well as the current status of the DataSource
. |
java.util.concurrent.Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest request,
AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
Returns a
DataSource that includes metadata and data file
information, as well as the current status of the DataSource
. |
java.util.concurrent.Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest request)
Returns an
Evaluation that includes metadata as well as the
current status of the Evaluation . |
java.util.concurrent.Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest request,
AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
Returns an
Evaluation that includes metadata as well as the
current status of the Evaluation . |
java.util.concurrent.ExecutorService |
getExecutorService()
Returns the executor service used by this client to execute async
requests.
|
java.util.concurrent.Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest request)
Returns an
MLModel that includes detailed metadata, and data
source information as well as the current status of the
MLModel . |
java.util.concurrent.Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest request,
AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an
MLModel that includes detailed metadata, and data
source information as well as the current status of the
MLModel . |
java.util.concurrent.Future<PredictResult> |
predictAsync(PredictRequest request)
Generates a prediction for the observation using the specified
ML Model . |
java.util.concurrent.Future<PredictResult> |
predictAsync(PredictRequest request,
AsyncHandler<PredictRequest,PredictResult> asyncHandler)
Generates a prediction for the observation using the specified
ML Model . |
void |
shutdown()
Shuts down the client, releasing all managed resources.
|
java.util.concurrent.Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest request)
Updates the
BatchPredictionName of a
BatchPrediction . |
java.util.concurrent.Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest request,
AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
Updates the
BatchPredictionName of a
BatchPrediction . |
java.util.concurrent.Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest request)
Updates the
DataSourceName of a DataSource . |
java.util.concurrent.Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest request,
AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
Updates the
DataSourceName of a DataSource . |
java.util.concurrent.Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest request)
Updates the
EvaluationName of an Evaluation . |
java.util.concurrent.Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest request,
AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
Updates the
EvaluationName of an Evaluation . |
java.util.concurrent.Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest request)
Updates the
MLModelName and the ScoreThreshold
of an MLModel . |
java.util.concurrent.Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest request,
AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
Updates the
MLModelName and the ScoreThreshold
of an MLModel . |
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, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel
addRequestHandler, addRequestHandler, beforeMarshalling, configureRegion, createExecutionContext, createExecutionContext, createExecutionContext, endClientExecution, endClientExecution, findRequestMetricCollector, getEndpointPrefix, getRequestMetricsCollector, getServiceAbbreviation, getServiceName, getServiceNameIntern, getSigner, getSignerByURI, getSignerRegionOverride, getTimeOffset, isProfilingEnabled, isRequestMetricsEnabled, removeRequestHandler, removeRequestHandler, requestMetricCollector, setEndpoint, setEndpointPrefix, setRegion, setServiceNameIntern, setSignerRegionOverride, setTimeOffset, withEndpoint, withRegion, withRegion, withTimeOffset
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel
private static final int DEFAULT_THREAD_POOL_SIZE
private final java.util.concurrent.ExecutorService executorService
public AmazonMachineLearningAsyncClient()
Asynchronous methods are delegated to a fixed-size thread pool containing 50 threads (to match the default maximum number of concurrent connections to the service).
DefaultAWSCredentialsProviderChain
,
Executors.newFixedThreadPool(int)
public AmazonMachineLearningAsyncClient(ClientConfiguration clientConfiguration)
Asynchronous methods are delegated to a fixed-size thread pool containing
a number of threads equal to the maximum number of concurrent connections
configured via ClientConfiguration.getMaxConnections()
.
clientConfiguration
- The client configuration options controlling how this client
connects to Amazon Machine Learning (ex: proxy settings, retry
counts, etc).DefaultAWSCredentialsProviderChain
,
Executors.newFixedThreadPool(int)
public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials)
Asynchronous methods are delegated to a fixed-size thread pool containing 50 threads (to match the default maximum number of concurrent connections to the service).
awsCredentials
- The AWS credentials (access key ID and secret key) to use when
authenticating with AWS services.Executors.newFixedThreadPool(int)
public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials, java.util.concurrent.ExecutorService executorService)
awsCredentials
- The AWS credentials (access key ID and secret key) to use when
authenticating with AWS services.executorService
- The executor service by which all asynchronous requests will be
executed.public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials, ClientConfiguration clientConfiguration, java.util.concurrent.ExecutorService executorService)
awsCredentials
- The AWS credentials (access key ID and secret key) to use when
authenticating with AWS services.clientConfiguration
- Client configuration options (ex: max retry limit, proxy settings,
etc).executorService
- The executor service by which all asynchronous requests will be
executed.public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider)
Asynchronous methods are delegated to a fixed-size thread pool containing 50 threads (to match the default maximum number of concurrent connections to the service).
awsCredentialsProvider
- The AWS credentials provider which will provide credentials to
authenticate requests with AWS services.Executors.newFixedThreadPool(int)
public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration)
Asynchronous methods are delegated to a fixed-size thread pool containing
a number of threads equal to the maximum number of concurrent connections
configured via ClientConfiguration.getMaxConnections()
.
awsCredentialsProvider
- The AWS credentials provider which will provide credentials to
authenticate requests with AWS services.clientConfiguration
- Client configuration options (ex: max retry limit, proxy settings,
etc).DefaultAWSCredentialsProviderChain
,
Executors.newFixedThreadPool(int)
public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, java.util.concurrent.ExecutorService executorService)
awsCredentialsProvider
- The AWS credentials provider which will provide credentials to
authenticate requests with AWS services.executorService
- The executor service by which all asynchronous requests will be
executed.public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration, java.util.concurrent.ExecutorService executorService)
awsCredentialsProvider
- The AWS credentials provider which will provide credentials to
authenticate requests with AWS services.clientConfiguration
- Client configuration options (ex: max retry limit, proxy settings,
etc).executorService
- The executor service by which all asynchronous requests will be
executed.public java.util.concurrent.ExecutorService getExecutorService()
public java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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
.
Caution: The results of the DeleteEvaluation
operation are irreversible.
deleteEvaluationAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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
.
Caution: The results of the DeleteEvaluation
operation are irreversible.
deleteEvaluationAsync
in interface AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request)
AmazonMachineLearningAsync
Returns a list of BatchPrediction
operations that match the
search criteria in the request.
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest)
public java.util.concurrent.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest,
com.amazonaws.handlers.AsyncHandler)
public java.util.concurrent.Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request)
AmazonMachineLearningAsync
Returns a list of DataSource
that match the search criteria
in the request.
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DescribeDataSourcesResult> describeDataSourcesAsync()
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(DescribeDataSourcesRequest)
public java.util.concurrent.Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(DescribeDataSourcesRequest,
com.amazonaws.handlers.AsyncHandler)
public java.util.concurrent.Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest request)
AmazonMachineLearningAsync
Returns a list of DescribeEvaluations
that match the search
criteria in the request.
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DescribeEvaluationsResult> describeEvaluationsAsync()
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(DescribeEvaluationsRequest)
public java.util.concurrent.Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(DescribeEvaluationsRequest,
com.amazonaws.handlers.AsyncHandler)
public java.util.concurrent.Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest request)
AmazonMachineLearningAsync
Returns a list of MLModel
that match the search criteria in
the request.
describeMLModelsAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<DescribeMLModelsResult> describeMLModelsAsync()
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest)
public java.util.concurrent.Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest,
com.amazonaws.handlers.AsyncHandler)
public java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest request)
AmazonMachineLearningAsync
Returns an Evaluation
that includes metadata as well as the
current status of the Evaluation
.
getEvaluationAsync
in interface AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 java.util.concurrent.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 AmazonMachineLearningAsync
public java.util.concurrent.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 AmazonMachineLearningAsync
asyncHandler
- 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 void shutdown()
getExecutorService().shutdown()
followed by
getExecutorService().awaitTermination()
prior to calling this
method.shutdown
in interface AmazonMachineLearning
shutdown
in class AmazonWebServiceClient