public interface AmazonMachineLearningAsync extends AmazonMachineLearning
AsyncHandler can be
used to receive notification when an asynchronous operation completes.
Definition of the public APIs exposed by Amazon Machine Learning
ENDPOINT_PREFIX| Modifier and Type | Method and Description |
|---|---|
Future<AddTagsResult> |
addTagsAsync(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10.
|
Future<AddTagsResult> |
addTagsAsync(AddTagsRequest addTagsRequest,
AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
Adds one or more tags to an object, up to a limit of 10.
|
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 a database hosted on an Amazon
Redshift cluster. |
Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest,
AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a
DataSource from a database hosted on an Amazon
Redshift cluster. |
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 DataSource and
the recipe as information sources. |
Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the DataSource 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<DeleteTagsResult> |
deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object.
|
Future<DeleteTagsResult> |
deleteTagsAsync(DeleteTagsRequest deleteTagsRequest,
AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags associated with an ML object.
|
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync()
Simplified method form for invoking the DescribeBatchPredictions
operation.
|
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Simplified method form for invoking the DescribeBatchPredictions
operation with an AsyncHandler.
|
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()
Simplified method form for invoking the DescribeDataSources operation.
|
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Simplified method form for invoking the DescribeDataSources operation
with an AsyncHandler.
|
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()
Simplified method form for invoking the DescribeEvaluations operation.
|
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Simplified method form for invoking the DescribeEvaluations operation
with an AsyncHandler.
|
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()
Simplified method form for invoking the DescribeMLModels operation.
|
Future<DescribeMLModelsResult> |
describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Simplified method form for invoking the DescribeMLModels operation with
an AsyncHandler.
|
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<DescribeTagsResult> |
describeTagsAsync(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
|
Future<DescribeTagsResult> |
describeTagsAsync(DescribeTagsRequest describeTagsRequest,
AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
Describes one or more of the tags for your Amazon ML object.
|
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, data
source information, and the current status of the MLModel. |
Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest getMLModelRequest,
AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an
MLModel that includes detailed metadata, data
source information, and the current status of the MLModel. |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model. |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest,
AsyncHandler<PredictRequest,PredictResult> asyncHandler)
Generates a prediction for the observation using the specified
ML Model. |
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. |
addTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waitersFuture<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10. Each tag
consists of a key and an optional value. If you add a tag using a key
that is already associated with the ML object, AddTags
updates the tag's value.
addTagsRequest - Future<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
Adds one or more tags to an object, up to a limit of 10. Each tag
consists of a key and an optional value. If you add a tag using a key
that is already associated with the ML object, AddTags
updates the tag's value.
addTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest)
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 - Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest, AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
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 - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates a DataSource object from an Amazon Relational Database Service
(Amazon RDS). A DataSource references data that can be used
to perform CreateMLModel, CreateEvaluation, or
CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In
response to CreateDataSourceFromRDS, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used only to
perform >CreateMLModel>, CreateEvaluation
, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
createDataSourceFromRDSRequest - Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
Creates a DataSource object from an Amazon Relational Database Service
(Amazon RDS). A DataSource references data that can be used
to perform CreateMLModel, CreateEvaluation, or
CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In
response to CreateDataSourceFromRDS, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource is
created and ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used only to
perform >CreateMLModel>, CreateEvaluation
, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
createDataSourceFromRDSRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a DataSource from a database hosted on an Amazon
Redshift cluster. A DataSource references data that can be
used to perform either CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromRedshift is an asynchronous operation.
In response to CreateDataSourceFromRedshift, Amazon Machine
Learning (Amazon ML) immediately returns and sets the
DataSource status to PENDING. After the
DataSource is created and ready for use, Amazon ML sets the
Status parameter to COMPLETED.
DataSource in COMPLETED or PENDING
states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
query. Amazon ML executes an Unload command in Amazon
Redshift to transfer the result set of the SelectSqlQuery
query to S3StagingLocation.
After the DataSource has been created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also requires a recipe. A recipe describes how
each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the
variable be manipulated; for example, will it be combined with another
variable or will it be split apart into word combinations? The recipe
provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the
settings from an existing Amazon Redshift datasource to create a new
datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call.
Change the settings that you want to change and make sure that all
required fields have the appropriate values.
createDataSourceFromRedshiftRequest - Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a DataSource from a database hosted on an Amazon
Redshift cluster. A DataSource references data that can be
used to perform either CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromRedshift is an asynchronous operation.
In response to CreateDataSourceFromRedshift, Amazon Machine
Learning (Amazon ML) immediately returns and sets the
DataSource status to PENDING. After the
DataSource is created and ready for use, Amazon ML sets the
Status parameter to COMPLETED.
DataSource in COMPLETED or PENDING
states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon
Redshift cluster and should be specified by a SelectSqlQuery
query. Amazon ML executes an Unload command in Amazon
Redshift to transfer the result set of the SelectSqlQuery
query to S3StagingLocation.
After the DataSource has been created, it's ready for use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also requires a recipe. A recipe describes how
each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the
variable be manipulated; for example, will it be combined with another
variable or will it be split apart into word combinations? The recipe
provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the
settings from an existing Amazon Redshift datasource to create a new
datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call.
Change the settings that you want to change and make sure that all
required fields have the appropriate values.
createDataSourceFromRedshiftRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a DataSource object. A DataSource
references data that can be used to perform CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromS3 is an asynchronous operation. In
response to CreateDataSourceFromS3, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource has
been created and is ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used to
perform only CreateMLModel, CreateEvaluation or
CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observation data used in a DataSource should be ready to
use; that is, it should have a consistent structure, and missing data
values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3)
location, along with a schema that describes the data items by name and
type. The same schema must be used for all of the data files referenced
by the DataSource.
After the DataSource has been created, it's ready to use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also needs a recipe. A recipe describes how each
input variable will be used in training an MLModel. Will the
variable be included or excluded from training? Will the variable be
manipulated; for example, will it be combined with another variable or
will it be split apart into word combinations? The recipe provides
answers to these questions.
createDataSourceFromS3Request - Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
Creates a DataSource object. A DataSource
references data that can be used to perform CreateMLModel,
CreateEvaluation, or CreateBatchPrediction
operations.
CreateDataSourceFromS3 is an asynchronous operation. In
response to CreateDataSourceFromS3, Amazon Machine Learning
(Amazon ML) immediately returns and sets the DataSource
status to PENDING. After the DataSource has
been created and is ready for use, Amazon ML sets the Status
parameter to COMPLETED. DataSource in the
COMPLETED or PENDING state can be used to
perform only CreateMLModel, CreateEvaluation or
CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the
Status parameter to FAILED and includes an
error message in the Message attribute of the
GetDataSource operation response.
The observation data used in a DataSource should be ready to
use; that is, it should have a consistent structure, and missing data
values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3)
location, along with a schema that describes the data items by name and
type. The same schema must be used for all of the data files referenced
by the DataSource.
After the DataSource has been created, it's ready to use in
evaluations and batch predictions. If you plan to use the
DataSource to train an MLModel, the
DataSource also needs a recipe. A recipe describes how each
input variable will be used in training an MLModel. Will the
variable be included or excluded from training? Will the variable be
manipulated; for example, will it be combined with another variable or
will it be split apart into word combinations? The recipe provides
answers to these questions.
createDataSourceFromS3Request - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest)
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 - Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest, AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
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 - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new MLModel using the DataSource and
the recipe as information sources.
An MLModel is nearly immutable. Users can update only the
MLModelName and the ScoreThreshold in an
MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to
CreateMLModel, Amazon Machine Learning (Amazon ML)
immediately returns and sets the MLModel status to
PENDING. After the MLModel has been created and
ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress
of the MLModel during the creation operation.
CreateMLModel requires a DataSource with
computed statistics, which can be created by setting
ComputeStatistics to true in
CreateDataSourceFromRDS, CreateDataSourceFromS3
, or CreateDataSourceFromRedshift operations.
createMLModelRequest - Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new MLModel using the DataSource and
the recipe as information sources.
An MLModel is nearly immutable. Users can update only the
MLModelName and the ScoreThreshold in an
MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to
CreateMLModel, Amazon Machine Learning (Amazon ML)
immediately returns and sets the MLModel status to
PENDING. After the MLModel has been created and
ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress
of the MLModel during the creation operation.
CreateMLModel requires a DataSource with
computed statistics, which can be created by setting
ComputeStatistics to true in
CreateDataSourceFromRDS, CreateDataSourceFromS3
, or CreateDataSourceFromRedshift operations.
createMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
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 - Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest, AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
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 - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a BatchPrediction, rendering
it unusable.
After using the DeleteBatchPrediction operation, you can use
the GetBatchPrediction operation to verify that the status of the
BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
deleteBatchPredictionRequest - Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest, AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
Assigns the DELETED status to a BatchPrediction, rendering
it unusable.
After using the DeleteBatchPrediction operation, you can use
the GetBatchPrediction operation to verify that the status of the
BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
deleteBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a DataSource, rendering it
unusable.
After using the DeleteDataSource operation, you can use the
GetDataSource operation to verify that the status of the
DataSource changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteDataSourceRequest - Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest, AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
Assigns the DELETED status to a DataSource, rendering it
unusable.
After using the DeleteDataSource operation, you can use the
GetDataSource operation to verify that the status of the
DataSource changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteDataSourceRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the DELETED status to an Evaluation,
rendering it unusable.
After invoking the DeleteEvaluation operation, you can use
the GetEvaluation operation to verify that the status of the
Evaluation changed to DELETED.
The results of the DeleteEvaluation operation are
irreversible.
deleteEvaluationRequest - Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest, AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
Assigns the DELETED status to an Evaluation,
rendering it unusable.
After invoking the DeleteEvaluation operation, you can use
the GetEvaluation operation to verify that the status of the
Evaluation changed to DELETED.
The results of the DeleteEvaluation operation are
irreversible.
deleteEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest)
Assigns the DELETED status to an MLModel,
rendering it unusable.
After using the DeleteMLModel operation, you can use the
GetMLModel operation to verify that the status of the
MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is
irreversible.
deleteMLModelRequest - Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest, AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
Assigns the DELETED status to an MLModel,
rendering it unusable.
After using the DeleteMLModel operation, you can use the
GetMLModel operation to verify that the status of the
MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is
irreversible.
deleteMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointRequest - Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsRequest - Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of BatchPrediction operations that match the
search criteria in the request.
describeBatchPredictionsRequest - Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest, AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Returns a list of BatchPrediction operations that match the
search criteria in the request.
describeBatchPredictionsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of DataSource that match the search criteria
in the request.
describeDataSourcesRequest - Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest, AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Returns a list of DataSource that match the search criteria
in the request.
describeDataSourcesRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeDataSourcesResult> describeDataSourcesAsync()
Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of DescribeEvaluations that match the search
criteria in the request.
describeEvaluationsRequest - Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest, AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Returns a list of DescribeEvaluations that match the search
criteria in the request.
describeEvaluationsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeEvaluationsResult> describeEvaluationsAsync()
Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of MLModel that match the search criteria in
the request.
describeMLModelsRequest - Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest, AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Returns a list of MLModel that match the search criteria in
the request.
describeMLModelsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeMLModelsResult> describeMLModelsAsync()
Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest - Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest describeTagsRequest, AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPredictionRequest - 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.
getBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest getDataSourceRequest)
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 - 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
.
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 - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest)
Returns an Evaluation that includes metadata as well as the
current status of the Evaluation.
getEvaluationRequest - Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest, AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
Returns an Evaluation that includes metadata as well as the
current status of the Evaluation.
getEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest)
Returns an MLModel that includes detailed metadata, data
source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelRequest - Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an MLModel that includes detailed metadata, data
source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<PredictResult> predictAsync(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictRequest - Future<PredictResult> predictAsync(PredictRequest predictRequest, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
Generates a prediction for the observation using the specified
ML Model.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the BatchPredictionName of a
BatchPrediction.
You can use the GetBatchPrediction operation to view the
contents of the updated data element.
updateBatchPredictionRequest - Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest, AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
Updates the BatchPredictionName of a
BatchPrediction.
You can use the GetBatchPrediction operation to view the
contents of the updated data element.
updateBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest)
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents
of the updated data element.
updateDataSourceRequest - Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest, AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents
of the updated data element.
updateDataSourceRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest)
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents
of the updated data element.
updateEvaluationRequest - Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest, AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents
of the updated data element.
updateEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest)
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 - Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest, AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
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 - asyncHandler - Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.