@ThreadSafe @Generated(value="com.amazonaws:aws-java-sdk-code-generator") 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
LOGGING_AWS_REQUEST_METRIC
ENDPOINT_PREFIX
Constructor and Description |
---|
AmazonMachineLearningAsyncClient()
Deprecated.
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials)
Deprecated.
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials,
ClientConfiguration clientConfiguration,
ExecutorService executorService)
|
AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials,
ExecutorService executorService)
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider)
Deprecated.
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration)
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration,
ExecutorService executorService)
|
AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider,
ExecutorService executorService)
|
AmazonMachineLearningAsyncClient(ClientConfiguration clientConfiguration)
Deprecated.
|
addTags, builder, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waiters
addRequestHandler, addRequestHandler, configureRegion, getClientConfiguration, getEndpointPrefix, getMonitoringListeners, getRequestMetricsCollector, getServiceName, getSignerByURI, getSignerOverride, getSignerRegionOverride, getTimeOffset, makeImmutable, removeRequestHandler, removeRequestHandler, setEndpoint, setEndpoint, setRegion, setServiceNameIntern, setSignerRegionOverride, setTimeOffset, withEndpoint, withRegion, withRegion, withTimeOffset
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
addTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waiters
@Deprecated public AmazonMachineLearningAsyncClient()
AmazonMachineLearningAsyncClientBuilder.defaultClient()
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).
@Deprecated public AmazonMachineLearningAsyncClient(ClientConfiguration clientConfiguration)
AwsClientBuilder.withClientConfiguration(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)
@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials)
AwsClientBuilder.withCredentials(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).
awsCredentials
- The AWS credentials (access key ID and secret key) to use when authenticating with AWS services.Executors.newFixedThreadPool(int)
@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials, ExecutorService executorService)
AwsClientBuilder.withCredentials(AWSCredentialsProvider)
and
AwsAsyncClientBuilder.withExecutorFactory(com.amazonaws.client.builder.ExecutorFactory)
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.@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentials awsCredentials, ClientConfiguration clientConfiguration, ExecutorService executorService)
AwsClientBuilder.withCredentials(AWSCredentialsProvider)
and
AwsClientBuilder.withClientConfiguration(ClientConfiguration)
and
AwsAsyncClientBuilder.withExecutorFactory(com.amazonaws.client.builder.ExecutorFactory)
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.@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider)
AwsClientBuilder.withCredentials(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)
@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration)
AwsClientBuilder.withCredentials(AWSCredentialsProvider)
and
AwsClientBuilder.withClientConfiguration(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)
@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, ExecutorService executorService)
AwsClientBuilder.withCredentials(AWSCredentialsProvider)
and
AwsAsyncClientBuilder.withExecutorFactory(com.amazonaws.client.builder.ExecutorFactory)
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.@Deprecated public AmazonMachineLearningAsyncClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration, ExecutorService executorService)
AwsClientBuilder.withCredentials(AWSCredentialsProvider)
and
AwsClientBuilder.withClientConfiguration(ClientConfiguration)
and
AwsAsyncClientBuilder.withExecutorFactory(com.amazonaws.client.builder.ExecutorFactory)
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 static AmazonMachineLearningAsyncClientBuilder asyncBuilder()
public ExecutorService getExecutorService()
public Future<AddTagsResult> addTagsAsync(AddTagsRequest request)
AmazonMachineLearningAsync
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags
updates the tag's
value.
addTagsAsync
in interface AmazonMachineLearningAsync
public Future<AddTagsResult> addTagsAsync(AddTagsRequest request, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
AmazonMachineLearningAsync
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags
updates the tag's
value.
addTagsAsync
in interface 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 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 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 Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request)
AmazonMachineLearningAsync
Creates a DataSource
object from an Amazon Relational Database
Service (Amazon RDS). A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
the COMPLETED
or PENDING
state can be used only to perform
>CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
createDataSourceFromRDSAsync
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource
object from an Amazon Relational Database
Service (Amazon RDS). A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
the COMPLETED
or PENDING
state can be used only to perform
>CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
createDataSourceFromRDSAsync
in interface 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 Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request)
AmazonMachineLearningAsync
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
COMPLETED
or PENDING
states can be used to perform only CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel
.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing
datasource and copy the values to a CreateDataSource
call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshiftAsync
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
COMPLETED
or PENDING
states can be used to perform only CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel
.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing
datasource and copy the values to a CreateDataSource
call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshiftAsync
in interface 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 Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request)
AmazonMachineLearningAsync
Creates a DataSource
object. A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
has been created and is
ready for use, Amazon ML sets the Status
parameter to COMPLETED
.
DataSource
in the COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observation data used in a DataSource
should be ready to use; that is, it should have a
consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
describes the data items by name and type. The same schema must be used for all of the data files referenced by
the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3Async
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
AmazonMachineLearningAsync
Creates a DataSource
object. A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
has been created and is
ready for use, Amazon ML sets the Status
parameter to COMPLETED
.
DataSource
in the COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observation data used in a DataSource
should be ready to use; that is, it should have a
consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
describes the data items by name and type. The same schema must be used for all of the data files referenced by
the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3Async
in interface 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 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 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 Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request)
AmazonMachineLearningAsync
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the
ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to
COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the
creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by
setting ComputeStatistics
to true
in CreateDataSourceFromRDS
,
CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
createMLModelAsync
in interface AmazonMachineLearningAsync
public Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the
ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to
COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the
creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by
setting ComputeStatistics
to true
in CreateDataSourceFromRDS
,
CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
createMLModelAsync
in interface 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 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 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 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 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 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 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 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 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 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 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 Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
public 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 Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request)
AmazonMachineLearningAsync
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsAsync
in interface AmazonMachineLearningAsync
public Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
AmazonMachineLearningAsync
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsAsync
in interface 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 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 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 Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest)
public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest, com.amazonaws.handlers.AsyncHandler)
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request)
AmazonMachineLearningAsync
Returns a list of DataSource
that match the search criteria in the request.
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
public 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 Future<DescribeDataSourcesResult> describeDataSourcesAsync()
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(DescribeDataSourcesRequest)
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(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 AmazonMachineLearningAsync
public 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 Future<DescribeEvaluationsResult> describeEvaluationsAsync()
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(DescribeEvaluationsRequest)
public Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(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 AmazonMachineLearningAsync
public 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 Future<DescribeMLModelsResult> describeMLModelsAsync()
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest)
public Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest, com.amazonaws.handlers.AsyncHandler)
public Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request)
AmazonMachineLearningAsync
Describes one or more of the tags for your Amazon ML object.
describeTagsAsync
in interface AmazonMachineLearningAsync
public Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request, AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
AmazonMachineLearningAsync
Describes one or more of the tags for your Amazon ML object.
describeTagsAsync
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 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 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 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 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 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 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 Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request)
AmazonMachineLearningAsync
Returns an MLModel
that includes detailed metadata, data source information, and the current status
of the MLModel
.
GetMLModel
provides results in normal or verbose format.
getMLModelAsync
in interface AmazonMachineLearningAsync
public Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Returns an MLModel
that includes detailed metadata, data source information, and the current status
of the MLModel
.
GetMLModel
provides results in normal or verbose format.
getMLModelAsync
in interface 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 Future<PredictResult> predictAsync(PredictRequest request)
AmazonMachineLearningAsync
Generates a prediction for the observation using the specified ML Model
.
Note: 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 Future<PredictResult> predictAsync(PredictRequest request, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
AmazonMachineLearningAsync
Generates a prediction for the observation using the specified ML Model
.
Note: 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 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 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 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 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 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 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 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 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 AmazonMachineLearningClient