Class AbstractAmazonMachineLearning
- All Implemented Interfaces:
AmazonMachineLearning
- Direct Known Subclasses:
AbstractAmazonMachineLearningAsync
AmazonMachineLearning
. Convenient method
forms pass through to the corresponding overload that takes a request object,
which throws an UnsupportedOperationException
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionGenerates predictions for a group of observations.Creates aDataSource
object from an Amazon Relational Database Service (Amazon RDS).Creates aDataSource
from Amazon Redshift.Creates aDataSource
object.createEvaluation
(CreateEvaluationRequest request) Creates a newEvaluation
of anMLModel
.createMLModel
(CreateMLModelRequest request) Creates a newMLModel
using the data files and the recipe as information sources.Creates a real-time endpoint for theMLModel
.Assigns the DELETED status to aBatchPrediction
, rendering it unusable.deleteDataSource
(DeleteDataSourceRequest request) Assigns the DELETED status to aDataSource
, rendering it unusable.deleteEvaluation
(DeleteEvaluationRequest request) Assigns theDELETED
status to anEvaluation
, rendering it unusable.deleteMLModel
(DeleteMLModelRequest request) Assigns the DELETED status to anMLModel
, rendering it unusable.Deletes a real time endpoint of anMLModel
.Simplified method form for invoking the DescribeBatchPredictions operation.Returns a list ofBatchPrediction
operations that match the search criteria in the request.Simplified method form for invoking the DescribeDataSources operation.Returns a list ofDataSource
that match the search criteria in the request.Simplified method form for invoking the DescribeEvaluations operation.Returns a list ofDescribeEvaluations
that match the search criteria in the request.Simplified method form for invoking the DescribeMLModels operation.describeMLModels
(DescribeMLModelsRequest request) Returns a list ofMLModel
that match the search criteria in the request.Returns aBatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.getDataSource
(GetDataSourceRequest request) Returns aDataSource
that includes metadata and data file information, as well as the current status of theDataSource
.getEvaluation
(GetEvaluationRequest request) Returns anEvaluation
that includes metadata as well as the current status of theEvaluation
.getMLModel
(GetMLModelRequest request) Returns anMLModel
that includes detailed metadata, and data source information as well as the current status of theMLModel
.predict
(PredictRequest request) Generates a prediction for the observation using the specifiedML Model
.void
setEndpoint
(String endpoint) Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com").void
An alternative toAmazonMachineLearning.setEndpoint(String)
, sets the regional endpoint for this client's service calls.void
shutdown()
Shuts down this client object, releasing any resources that might be held open.Updates theBatchPredictionName
of aBatchPrediction
.updateDataSource
(UpdateDataSourceRequest request) Updates theDataSourceName
of aDataSource
.updateEvaluation
(UpdateEvaluationRequest request) Updates theEvaluationName
of anEvaluation
.updateMLModel
(UpdateMLModelRequest request) Updates theMLModelName
and theScoreThreshold
of anMLModel
.
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Constructor Details
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AbstractAmazonMachineLearning
protected AbstractAmazonMachineLearning()
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Method Details
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setEndpoint
Description copied from interface:AmazonMachineLearning
Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com"). Callers can use this method to control which AWS region they want to work with.Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the default protocol from this client's
ClientConfiguration
will be used, which by default is HTTPS.For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available endpoints for all AWS services, see: http://developer.amazonwebservices.com/connect/entry.jspa?externalID= 3912
This method is not threadsafe. An endpoint should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
- Specified by:
setEndpoint
in interfaceAmazonMachineLearning
- Parameters:
endpoint
- The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will communicate with.
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setRegion
Description copied from interface:AmazonMachineLearning
An alternative toAmazonMachineLearning.setEndpoint(String)
, sets the regional endpoint for this client's service calls. Callers can use this method to control which AWS region they want to work with.By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the
ClientConfiguration
supplied at construction.This method is not threadsafe. A region should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
- Specified by:
setRegion
in interfaceAmazonMachineLearning
- Parameters:
region
- The region this client will communicate with. SeeRegion.getRegion(com.amazonaws.regions.Regions)
for accessing a given region. Must not be null and must be a region where the service is available.- See Also:
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createBatchPrediction
Description copied from interface:AmazonMachineLearning
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 newBatchPrediction
, and uses anMLModel
and the data files referenced by theDataSource
as information sources.CreateBatchPrediction
is an asynchronous operation. In response toCreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets theBatchPrediction
status toPENDING
. After theBatchPrediction
completes, Amazon ML sets the status toCOMPLETED
.You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After theCOMPLETED
status appears, the results are available in the location specified by theOutputUri
parameter.- Specified by:
createBatchPrediction
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateBatchPrediction operation returned by the service.
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createDataSourceFromRDS
public CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest request) Description copied from interface:AmazonMachineLearning
Creates a
DataSource
object from an Amazon Relational Database Service (Amazon RDS). ADataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.CreateDataSourceFromRDS
is an asynchronous operation. In response toCreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
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 toFAILED
and includes an error message in theMessage
attribute of the GetDataSource operation response.- Specified by:
createDataSourceFromRDS
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateDataSourceFromRDS operation returned by the service.
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createDataSourceFromRedshift
public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request) Description copied from interface:AmazonMachineLearning
Creates a
DataSource
from Amazon Redshift. ADataSource
references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.CreateDataSourceFromRedshift
is an asynchronous operation. In response toCreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
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 toFAILED
and includes an error message in theMessage
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 ofSelectSqlQuery
toS3StagingLocation.
After the
DataSource
is created, it's ready for use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
requires another item -- a recipe. A recipe describes the observation variables that participate in training anMLModel
. 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.- Specified by:
createDataSourceFromRedshift
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateDataSourceFromRedshift operation returned by the service.
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createDataSourceFromS3
Description copied from interface:AmazonMachineLearning
Creates a
DataSource
object. ADataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.CreateDataSourceFromS3
is an asynchronous operation. In response toCreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
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 toFAILED
and includes an error message in theMessage
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 theDataSource
.After the
DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
requires another item: a recipe. A recipe describes the observation variables that participate in training anMLModel
. 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.- Specified by:
createDataSourceFromS3
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateDataSourceFromS3 operation returned by the service.
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createEvaluation
Description copied from interface:AmazonMachineLearning
Creates a new
Evaluation
of anMLModel
. AnMLModel
is evaluated on a set of observations associated to aDataSource
. Like aDataSource
for anMLModel
, theDataSource
for anEvaluation
contains values for the Target Variable. TheEvaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective theMLModel
functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the correspondingMLModelType
:BINARY
,REGRESSION
orMULTICLASS
.CreateEvaluation
is an asynchronous operation. In response toCreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status toPENDING
. After theEvaluation
is created and ready for use, Amazon ML sets the status toCOMPLETED
.You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
- Specified by:
createEvaluation
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateEvaluation operation returned by the service.
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createMLModel
Description copied from interface:AmazonMachineLearning
Creates a new
MLModel
using the data files and the recipe as information sources.An
MLModel
is nearly immutable. Users can only update theMLModelName
and theScoreThreshold
in anMLModel
without creating a newMLModel
.CreateMLModel
is an asynchronous operation. In response toCreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets theMLModel
status toPENDING
. After theMLModel
is created and ready for use, Amazon ML sets the status toCOMPLETED
.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 settingComputeStatistics
totrue
in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.- Specified by:
createMLModel
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateMLModel operation returned by the service.
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createRealtimeEndpoint
Description copied from interface:AmazonMachineLearning
Creates a real-time endpoint for the
MLModel
. The endpoint contains the URI of theMLModel
; that is, the location to send real-time prediction requests for the specifiedMLModel
.- Specified by:
createRealtimeEndpoint
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the CreateRealtimeEndpoint operation returned by the service.
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deleteBatchPrediction
Description copied from interface:AmazonMachineLearning
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 theBatchPrediction
changed to DELETED.Caution: The result of the
DeleteBatchPrediction
operation is irreversible.- Specified by:
deleteBatchPrediction
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DeleteBatchPrediction operation returned by the service.
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deleteDataSource
Description copied from interface:AmazonMachineLearning
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 theDataSource
changed to DELETED.Caution: The results of the
DeleteDataSource
operation are irreversible.- Specified by:
deleteDataSource
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DeleteDataSource operation returned by the service.
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deleteEvaluation
Description copied from interface:AmazonMachineLearning
Assigns the
DELETED
status to anEvaluation
, rendering it unusable.After invoking the
DeleteEvaluation
operation, you can use the GetEvaluation operation to verify that the status of theEvaluation
changed toDELETED
.Caution: The results of the
DeleteEvaluation
operation are irreversible.- Specified by:
deleteEvaluation
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DeleteEvaluation operation returned by the service.
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deleteMLModel
Description copied from interface:AmazonMachineLearning
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 theMLModel
changed to DELETED.Caution: The result of the
DeleteMLModel
operation is irreversible.- Specified by:
deleteMLModel
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DeleteMLModel operation returned by the service.
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deleteRealtimeEndpoint
Description copied from interface:AmazonMachineLearning
Deletes a real time endpoint of an
MLModel
.- Specified by:
deleteRealtimeEndpoint
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DeleteRealtimeEndpoint operation returned by the service.
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describeBatchPredictions
public DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request) Description copied from interface:AmazonMachineLearning
Returns a list of
BatchPrediction
operations that match the search criteria in the request.- Specified by:
describeBatchPredictions
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DescribeBatchPredictions operation returned by the service.
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describeBatchPredictions
Description copied from interface:AmazonMachineLearning
Simplified method form for invoking the DescribeBatchPredictions operation.- Specified by:
describeBatchPredictions
in interfaceAmazonMachineLearning
- See Also:
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describeDataSources
Description copied from interface:AmazonMachineLearning
Returns a list of
DataSource
that match the search criteria in the request.- Specified by:
describeDataSources
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DescribeDataSources operation returned by the service.
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describeDataSources
Description copied from interface:AmazonMachineLearning
Simplified method form for invoking the DescribeDataSources operation.- Specified by:
describeDataSources
in interfaceAmazonMachineLearning
- See Also:
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describeEvaluations
Description copied from interface:AmazonMachineLearning
Returns a list of
DescribeEvaluations
that match the search criteria in the request.- Specified by:
describeEvaluations
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DescribeEvaluations operation returned by the service.
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describeEvaluations
Description copied from interface:AmazonMachineLearning
Simplified method form for invoking the DescribeEvaluations operation.- Specified by:
describeEvaluations
in interfaceAmazonMachineLearning
- See Also:
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describeMLModels
Description copied from interface:AmazonMachineLearning
Returns a list of
MLModel
that match the search criteria in the request.- Specified by:
describeMLModels
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the DescribeMLModels operation returned by the service.
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describeMLModels
Description copied from interface:AmazonMachineLearning
Simplified method form for invoking the DescribeMLModels operation.- Specified by:
describeMLModels
in interfaceAmazonMachineLearning
- See Also:
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getBatchPrediction
Description copied from interface:AmazonMachineLearning
Returns a
BatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.- Specified by:
getBatchPrediction
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the GetBatchPrediction operation returned by the service.
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getDataSource
Description copied from interface:AmazonMachineLearning
Returns a
DataSource
that includes metadata and data file information, as well as the current status of theDataSource
.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.- Specified by:
getDataSource
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the GetDataSource operation returned by the service.
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getEvaluation
Description copied from interface:AmazonMachineLearning
Returns an
Evaluation
that includes metadata as well as the current status of theEvaluation
.- Specified by:
getEvaluation
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the GetEvaluation operation returned by the service.
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getMLModel
Description copied from interface:AmazonMachineLearning
Returns an
MLModel
that includes detailed metadata, and data source information as well as the current status of theMLModel
.GetMLModel
provides results in normal or verbose format.- Specified by:
getMLModel
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the GetMLModel operation returned by the service.
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predict
Description copied from interface:AmazonMachineLearning
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.
- Specified by:
predict
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the Predict operation returned by the service.
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updateBatchPrediction
Description copied from interface:AmazonMachineLearning
Updates the
BatchPredictionName
of aBatchPrediction
.You can use the GetBatchPrediction operation to view the contents of the updated data element.
- Specified by:
updateBatchPrediction
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the UpdateBatchPrediction operation returned by the service.
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updateDataSource
Description copied from interface:AmazonMachineLearning
Updates the
DataSourceName
of aDataSource
.You can use the GetDataSource operation to view the contents of the updated data element.
- Specified by:
updateDataSource
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the UpdateDataSource operation returned by the service.
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updateEvaluation
Description copied from interface:AmazonMachineLearning
Updates the
EvaluationName
of anEvaluation
.You can use the GetEvaluation operation to view the contents of the updated data element.
- Specified by:
updateEvaluation
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the UpdateEvaluation operation returned by the service.
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updateMLModel
Description copied from interface:AmazonMachineLearning
Updates the
MLModelName
and theScoreThreshold
of anMLModel
.You can use the GetMLModel operation to view the contents of the updated data element.
- Specified by:
updateMLModel
in interfaceAmazonMachineLearning
- Parameters:
request
-- Returns:
- Result of the UpdateMLModel operation returned by the service.
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shutdown
public void shutdown()Description copied from interface:AmazonMachineLearning
Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.- Specified by:
shutdown
in interfaceAmazonMachineLearning
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getCachedResponseMetadata
Description copied from interface:AmazonMachineLearning
Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
- Specified by:
getCachedResponseMetadata
in interfaceAmazonMachineLearning
- Parameters:
request
- The originally executed request.- Returns:
- The response metadata for the specified request, or null if none is available.
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