public interface AmazonMachineLearningAsync extends AmazonMachineLearning
Definition of the public APIs exposed by Amazon Machine Learning
| Modifier and Type | Method and Description |
|---|---|
Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations.
|
Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest,
AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
Generates predictions for a group of observations.
|
Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates a
DataSource object from an
Amazon Relational Database Service
(Amazon RDS). |
Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest,
AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
Creates a
DataSource object from an
Amazon Relational Database Service
(Amazon RDS). |
Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a
DataSource from
Amazon Redshift
. |
Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest,
AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a
DataSource from
Amazon Redshift
. |
Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a
DataSource object. |
Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request,
AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
Creates a
DataSource object. |
Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest)
Creates a new
Evaluation of an MLModel . |
Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest,
AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
Creates a new
Evaluation of an MLModel . |
Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel . |
Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest,
AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
Creates a real-time endpoint for the
MLModel . |
Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a
BatchPrediction ,
rendering it unusable. |
Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest,
AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
Assigns the DELETED status to a
BatchPrediction ,
rendering it unusable. |
Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a
DataSource , rendering
it unusable. |
Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest,
AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
Assigns the DELETED status to a
DataSource , rendering
it unusable. |
Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the
DELETED status to an Evaluation
, rendering it unusable. |
Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest,
AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
Assigns the
DELETED status to an Evaluation
, rendering it unusable. |
Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest)
Assigns the DELETED status to an
MLModel , rendering it
unusable. |
Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest,
AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
Assigns the DELETED status to an
MLModel , rendering it
unusable. |
Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel . |
Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest,
AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
Deletes a real time endpoint of an
MLModel . |
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match
the search criteria in the request. |
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest,
AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Returns a list of
BatchPrediction operations that match
the search criteria in the request. |
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource that match the search
criteria in the request. |
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest,
AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Returns a list of
DataSource that match the search
criteria in the request. |
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the
search criteria in the request. |
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest,
AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Returns a list of
DescribeEvaluations that match the
search criteria in the request. |
Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of
MLModel that match the search criteria
in the request. |
Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest,
AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Returns a list of
MLModel that match the search criteria
in the request. |
Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a
BatchPrediction that includes detailed
metadata, status, and data file information for a Batch
Prediction request. |
Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest,
AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
Returns a
BatchPrediction that includes detailed
metadata, status, and data file information for a Batch
Prediction request. |
Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest getDataSourceRequest)
Returns a
DataSource that includes metadata and data
file information, as well as the current status of the
DataSource . |
Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest getDataSourceRequest,
AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
Returns a
DataSource that includes metadata and data
file information, as well as the current status of the
DataSource . |
Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest getEvaluationRequest)
Returns an
Evaluation that includes metadata as well as
the current status of the Evaluation . |
Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest getEvaluationRequest,
AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
Returns an
Evaluation that includes metadata as well as
the current status of the Evaluation . |
Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest getMLModelRequest)
Returns an
MLModel that includes detailed metadata, and
data source information as well as the current status of the
MLModel . |
Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest getMLModelRequest,
AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an
MLModel that includes detailed metadata, and
data source information as well as the current status of the
MLModel . |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
MLModel . |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest,
AsyncHandler<PredictRequest,PredictResult> asyncHandler)
Generates a prediction for the observation using the specified
MLModel . |
Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the
BatchPredictionName of a
BatchPrediction . |
Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest,
AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
Updates the
BatchPredictionName of a
BatchPrediction . |
Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest)
Updates the
DataSourceName of a DataSource
. |
Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest,
AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
Updates the
DataSourceName of a DataSource
. |
Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest)
Updates the
EvaluationName of an Evaluation
. |
Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest,
AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
Updates the
EvaluationName of an Evaluation
. |
Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest)
Updates the
MLModelName and the
ScoreThreshold of an MLModel . |
Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest,
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, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModelFuture<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest) throws AmazonServiceException, AmazonClientException
Updates the EvaluationName of an Evaluation
.
You can use the GetEvaluation operation to view the contents of the updated data element.
updateEvaluationRequest - Container for the necessary parameters
to execute the UpdateEvaluation operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest, AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Updates the EvaluationName of an Evaluation
.
You can use the GetEvaluation operation to view the contents of the updated data element.
updateEvaluationRequest - Container for the necessary parameters
to execute the UpdateEvaluation operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest) throws AmazonServiceException, AmazonClientException
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.
createMLModelRequest - Container for the necessary parameters to
execute the CreateMLModel operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
createMLModelRequest - Container for the necessary parameters to
execute the CreateMLModel operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest) throws AmazonServiceException, AmazonClientException
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 .
createRealtimeEndpointRequest - Container for the necessary
parameters to execute the CreateRealtimeEndpoint operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest, AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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 .
createRealtimeEndpointRequest - Container for the necessary
parameters to execute the CreateRealtimeEndpoint operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request) throws AmazonServiceException, AmazonClientException
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
.
createDataSourceFromS3Request - Container for the necessary
parameters to execute the CreateDataSourceFromS3 operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler) throws AmazonServiceException, AmazonClientException
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
.
createDataSourceFromS3Request - Container for the necessary
parameters to execute the CreateDataSourceFromS3 operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest) throws AmazonServiceException, AmazonClientException
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.
deleteMLModelRequest - Container for the necessary parameters to
execute the DeleteMLModel operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest, AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
deleteMLModelRequest - Container for the necessary parameters to
execute the DeleteMLModel operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<PredictResult> predictAsync(PredictRequest predictRequest) throws AmazonServiceException, AmazonClientException
Generates a prediction for the observation using the specified
MLModel .
NOTE: Note Not all response parameters will be populated because this is dependent on the type of requested model.
predictRequest - Container for the necessary parameters to
execute the Predict operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<PredictResult> predictAsync(PredictRequest predictRequest, AsyncHandler<PredictRequest,PredictResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Generates a prediction for the observation using the specified
MLModel .
NOTE: Note Not all response parameters will be populated because this is dependent on the type of requested model.
predictRequest - Container for the necessary parameters to
execute the Predict operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest) throws AmazonServiceException, AmazonClientException
Returns a list of BatchPrediction operations that match
the search criteria in the request.
describeBatchPredictionsRequest - Container for the necessary
parameters to execute the DescribeBatchPredictions operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest, AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns a list of BatchPrediction operations that match
the search criteria in the request.
describeBatchPredictionsRequest - Container for the necessary
parameters to execute the DescribeBatchPredictions operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest) throws AmazonServiceException, AmazonClientException
Returns an Evaluation that includes metadata as well as
the current status of the Evaluation .
getEvaluationRequest - Container for the necessary parameters to
execute the GetEvaluation operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest, AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns an Evaluation that includes metadata as well as
the current status of the Evaluation .
getEvaluationRequest - Container for the necessary parameters to
execute the GetEvaluation operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest) throws AmazonServiceException, AmazonClientException
Updates the MLModelName and the
ScoreThreshold of an MLModel .
You can use the GetMLModel operation to view the contents of the updated data element.
updateMLModelRequest - Container for the necessary parameters to
execute the UpdateMLModel operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest, AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Updates the MLModelName and the
ScoreThreshold of an MLModel .
You can use the GetMLModel operation to view the contents of the updated data element.
updateMLModelRequest - Container for the necessary parameters to
execute the UpdateMLModel operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest getDataSourceRequest) throws AmazonServiceException, AmazonClientException
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.
getDataSourceRequest - Container for the necessary parameters to
execute the GetDataSource operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest getDataSourceRequest, AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
getDataSourceRequest - Container for the necessary parameters to
execute the GetDataSource operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest) throws AmazonServiceException, AmazonClientException
Returns a list of DataSource that match the search
criteria in the request.
describeDataSourcesRequest - Container for the necessary
parameters to execute the DescribeDataSources operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest, AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns a list of DataSource that match the search
criteria in the request.
describeDataSourcesRequest - Container for the necessary
parameters to execute the DescribeDataSources operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest) throws AmazonServiceException, AmazonClientException
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 .
deleteEvaluationRequest - Container for the necessary parameters
to execute the DeleteEvaluation operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest, AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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 .
deleteEvaluationRequest - Container for the necessary parameters
to execute the DeleteEvaluation operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest) throws AmazonServiceException, AmazonClientException
Updates the BatchPredictionName of a
BatchPrediction .
You can use the GetBatchPrediction operation to view the contents of the updated data element.
updateBatchPredictionRequest - Container for the necessary
parameters to execute the UpdateBatchPrediction operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest, AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Updates the BatchPredictionName of a
BatchPrediction .
You can use the GetBatchPrediction operation to view the contents of the updated data element.
updateBatchPredictionRequest - Container for the necessary
parameters to execute the UpdateBatchPrediction operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest) throws AmazonServiceException, AmazonClientException
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.
createBatchPredictionRequest - Container for the necessary
parameters to execute the CreateBatchPrediction operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest, AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
createBatchPredictionRequest - Container for the necessary
parameters to execute the CreateBatchPrediction operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest) throws AmazonServiceException, AmazonClientException
Returns a list of MLModel that match the search criteria
in the request.
describeMLModelsRequest - Container for the necessary parameters
to execute the DescribeMLModels operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest, AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns a list of MLModel that match the search criteria
in the request.
describeMLModelsRequest - Container for the necessary parameters
to execute the DescribeMLModels operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest) throws AmazonServiceException, AmazonClientException
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.
deleteBatchPredictionRequest - Container for the necessary
parameters to execute the DeleteBatchPrediction operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest, AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
deleteBatchPredictionRequest - Container for the necessary
parameters to execute the DeleteBatchPrediction operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest) throws AmazonServiceException, AmazonClientException
Updates the DataSourceName of a DataSource
.
You can use the GetDataSource operation to view the contents of the updated data element.
updateDataSourceRequest - Container for the necessary parameters
to execute the UpdateDataSource operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest, AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Updates the DataSourceName of a DataSource
.
You can use the GetDataSource operation to view the contents of the updated data element.
updateDataSourceRequest - Container for the necessary parameters
to execute the UpdateDataSource operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest) throws AmazonServiceException, AmazonClientException
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.
createDataSourceFromRDSRequest - Container for the necessary
parameters to execute the CreateDataSourceFromRDS operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
createDataSourceFromRDSRequest - Container for the necessary
parameters to execute the CreateDataSourceFromRDS operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest) throws AmazonServiceException, AmazonClientException
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.
createDataSourceFromRedshiftRequest - Container for the necessary
parameters to execute the CreateDataSourceFromRedshift operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
createDataSourceFromRedshiftRequest - Container for the necessary
parameters to execute the CreateDataSourceFromRedshift operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest) throws AmazonServiceException, AmazonClientException
Returns a list of DescribeEvaluations that match the
search criteria in the request.
describeEvaluationsRequest - Container for the necessary
parameters to execute the DescribeEvaluations operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest, AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns a list of DescribeEvaluations that match the
search criteria in the request.
describeEvaluationsRequest - Container for the necessary
parameters to execute the DescribeEvaluations operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest) throws AmazonServiceException, AmazonClientException
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.
getMLModelRequest - Container for the necessary parameters to
execute the GetMLModel operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
getMLModelRequest - Container for the necessary parameters to
execute the GetMLModel operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest) throws AmazonServiceException, AmazonClientException
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.
deleteDataSourceRequest - Container for the necessary parameters
to execute the DeleteDataSource operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest, AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
deleteDataSourceRequest - Container for the necessary parameters
to execute the DeleteDataSource operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest) throws AmazonServiceException, AmazonClientException
Returns a BatchPrediction that includes detailed
metadata, status, and data file information for a Batch
Prediction request.
getBatchPredictionRequest - Container for the necessary
parameters to execute the GetBatchPrediction operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest, AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Returns a BatchPrediction that includes detailed
metadata, status, and data file information for a Batch
Prediction request.
getBatchPredictionRequest - Container for the necessary
parameters to execute the GetBatchPrediction operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest) throws AmazonServiceException, AmazonClientException
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.
createEvaluationRequest - Container for the necessary parameters
to execute the CreateEvaluation operation on AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest, AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler) throws AmazonServiceException, AmazonClientException
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.
createEvaluationRequest - Container for the necessary parameters
to execute the CreateEvaluation operation on AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest) throws AmazonServiceException, AmazonClientException
Deletes a real time endpoint of an MLModel .
deleteRealtimeEndpointRequest - Container for the necessary
parameters to execute the DeleteRealtimeEndpoint operation on
AmazonMachineLearning.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler) throws AmazonServiceException, AmazonClientException
Deletes a real time endpoint of an MLModel .
deleteRealtimeEndpointRequest - Container for the necessary
parameters to execute the DeleteRealtimeEndpoint operation on
AmazonMachineLearning.asyncHandler - Asynchronous callback handler for events in the
life-cycle of the request. Users could provide the implementation of
the four callback methods in this interface to process the operation
result or handle the exception.AmazonClientException - If any internal errors are encountered inside the client while
attempting to make the request or handle the response. For example
if a network connection is not available.AmazonServiceException - If an error response is returned by AmazonMachineLearning indicating
either a problem with the data in the request, or a server side issue.Copyright © 2015. All rights reserved.