Interface AmazonMachineLearning
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- All Known Subinterfaces:
AmazonMachineLearningAsync
- All Known Implementing Classes:
AbstractAmazonMachineLearning
,AbstractAmazonMachineLearningAsync
,AmazonMachineLearningAsyncClient
,AmazonMachineLearningClient
public interface AmazonMachineLearning
Interface for accessing Amazon Machine Learning.Definition of the public APIs exposed by Amazon Machine Learning
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description CreateBatchPredictionResult
createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations.CreateDataSourceFromRDSResult
createDataSourceFromRDS(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates aDataSource
object from an Amazon Relational Database Service (Amazon RDS).CreateDataSourceFromRedshiftResult
createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates aDataSource
from Amazon Redshift.CreateDataSourceFromS3Result
createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates aDataSource
object.CreateEvaluationResult
createEvaluation(CreateEvaluationRequest createEvaluationRequest)
Creates a newEvaluation
of anMLModel
.CreateMLModelResult
createMLModel(CreateMLModelRequest createMLModelRequest)
Creates a newMLModel
using the data files and the recipe as information sources.CreateRealtimeEndpointResult
createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for theMLModel
.DeleteBatchPredictionResult
deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to aBatchPrediction
, rendering it unusable.DeleteDataSourceResult
deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to aDataSource
, rendering it unusable.DeleteEvaluationResult
deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns theDELETED
status to anEvaluation
, rendering it unusable.DeleteMLModelResult
deleteMLModel(DeleteMLModelRequest deleteMLModelRequest)
Assigns the DELETED status to anMLModel
, rendering it unusable.DeleteRealtimeEndpointResult
deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of anMLModel
.DescribeBatchPredictionsResult
describeBatchPredictions()
Simplified method form for invoking the DescribeBatchPredictions operation.DescribeBatchPredictionsResult
describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list ofBatchPrediction
operations that match the search criteria in the request.DescribeDataSourcesResult
describeDataSources()
Simplified method form for invoking the DescribeDataSources operation.DescribeDataSourcesResult
describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list ofDataSource
that match the search criteria in the request.DescribeEvaluationsResult
describeEvaluations()
Simplified method form for invoking the DescribeEvaluations operation.DescribeEvaluationsResult
describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list ofDescribeEvaluations
that match the search criteria in the request.DescribeMLModelsResult
describeMLModels()
Simplified method form for invoking the DescribeMLModels operation.DescribeMLModelsResult
describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list ofMLModel
that match the search criteria in the request.GetBatchPredictionResult
getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns aBatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.ResponseMetadata
getCachedResponseMetadata(AmazonWebServiceRequest request)
Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.GetDataSourceResult
getDataSource(GetDataSourceRequest getDataSourceRequest)
Returns aDataSource
that includes metadata and data file information, as well as the current status of theDataSource
.GetEvaluationResult
getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns anEvaluation
that includes metadata as well as the current status of theEvaluation
.GetMLModelResult
getMLModel(GetMLModelRequest getMLModelRequest)
Returns anMLModel
that includes detailed metadata, and data source information as well as the current status of theMLModel
.PredictResult
predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specifiedML Model
.void
setEndpoint(String endpoint)
Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com").void
setRegion(Region region)
An alternative tosetEndpoint(String)
, sets the regional endpoint for this client's service calls.void
shutdown()
Shuts down this client object, releasing any resources that might be held open.UpdateBatchPredictionResult
updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates theBatchPredictionName
of aBatchPrediction
.UpdateDataSourceResult
updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
Updates theDataSourceName
of aDataSource
.UpdateEvaluationResult
updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
Updates theEvaluationName
of anEvaluation
.UpdateMLModelResult
updateMLModel(UpdateMLModelRequest updateMLModelRequest)
Updates theMLModelName
and theScoreThreshold
of anMLModel
.
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Method Detail
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setEndpoint
void setEndpoint(String endpoint)
Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com"). Callers can use this method to control which AWS region they want to work with.Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the default protocol from this client's
ClientConfiguration
will be used, which by default is HTTPS.For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available endpoints for all AWS services, see: http://developer.amazonwebservices.com/connect/entry.jspa?externalID= 3912
This method is not threadsafe. An endpoint should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
- Parameters:
endpoint
- The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will communicate with.
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setRegion
void setRegion(Region region)
An alternative tosetEndpoint(String)
, sets the regional endpoint for this client's service calls. Callers can use this method to control which AWS region they want to work with.By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the
ClientConfiguration
supplied at construction.This method is not threadsafe. A region should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
- Parameters:
region
- The region this client will communicate with. SeeRegion.getRegion(com.amazonaws.regions.Regions)
for accessing a given region. Must not be null and must be a region where the service is available.- See Also:
Region.getRegion(com.amazonaws.regions.Regions)
,Region.createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration)
,Region.isServiceSupported(String)
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createBatchPrediction
CreateBatchPredictionResult createBatchPrediction(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 newBatchPrediction
, and uses anMLModel
and the data files referenced by theDataSource
as information sources.CreateBatchPrediction
is an asynchronous operation. In response toCreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets theBatchPrediction
status toPENDING
. After theBatchPrediction
completes, Amazon ML sets the status toCOMPLETED
.You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After theCOMPLETED
status appears, the results are available in the location specified by theOutputUri
parameter.- Parameters:
createBatchPredictionRequest
-- Returns:
- Result of the CreateBatchPrediction operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createDataSourceFromRDS
CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates a
DataSource
object from an Amazon Relational Database Service (Amazon RDS). ADataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.CreateDataSourceFromRDS
is an asynchronous operation. In response toCreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.If Amazon ML cannot accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of the GetDataSource operation response.- Parameters:
createDataSourceFromRDSRequest
-- Returns:
- Result of the CreateDataSourceFromRDS operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createDataSourceFromRedshift
CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a
DataSource
from Amazon Redshift. ADataSource
references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.CreateDataSourceFromRedshift
is an asynchronous operation. In response toCreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.If Amazon ML cannot accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of the GetDataSource operation response.The observations should exist in the database hosted on an Amazon Redshift cluster and should be specified by a
SelectSqlQuery
. Amazon ML executes Unload command in Amazon Redshift to transfer the result set ofSelectSqlQuery
toS3StagingLocation.
After the
DataSource
is created, it's ready for use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
requires another item -- a recipe. A recipe describes the observation variables that participate in training anMLModel
. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.- Parameters:
createDataSourceFromRedshiftRequest
-- Returns:
- Result of the CreateDataSourceFromRedshift operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createDataSourceFromS3
CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a
DataSource
object. ADataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.CreateDataSourceFromS3
is an asynchronous operation. In response toCreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.If Amazon ML cannot accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of the GetDataSource operation response.The observation data used in a
DataSource
should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by theDataSource
.After the
DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
requires another item: a recipe. A recipe describes the observation variables that participate in training anMLModel
. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.- Parameters:
createDataSourceFromS3Request
-- Returns:
- Result of the CreateDataSourceFromS3 operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createEvaluation
CreateEvaluationResult createEvaluation(CreateEvaluationRequest createEvaluationRequest)
Creates a new
Evaluation
of anMLModel
. AnMLModel
is evaluated on a set of observations associated to aDataSource
. Like aDataSource
for anMLModel
, theDataSource
for anEvaluation
contains values for the Target Variable. TheEvaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective theMLModel
functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the correspondingMLModelType
:BINARY
,REGRESSION
orMULTICLASS
.CreateEvaluation
is an asynchronous operation. In response toCreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status toPENDING
. After theEvaluation
is created and ready for use, Amazon ML sets the status toCOMPLETED
.You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
- Parameters:
createEvaluationRequest
-- Returns:
- Result of the CreateEvaluation operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createMLModel
CreateMLModelResult createMLModel(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel
using the data files and the recipe as information sources.An
MLModel
is nearly immutable. Users can only update theMLModelName
and theScoreThreshold
in anMLModel
without creating a newMLModel
.CreateMLModel
is an asynchronous operation. In response toCreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets theMLModel
status toPENDING
. After theMLModel
is created and ready for use, Amazon ML sets the status toCOMPLETED
.You can use the GetMLModel operation to check progress of the
MLModel
during the creation operation.CreateMLModel requires a
DataSource
with computed statistics, which can be created by settingComputeStatistics
totrue
in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.- Parameters:
createMLModelRequest
-- Returns:
- Result of the CreateMLModel operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
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createRealtimeEndpoint
CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel
. The endpoint contains the URI of theMLModel
; that is, the location to send real-time prediction requests for the specifiedMLModel
.- Parameters:
createRealtimeEndpointRequest
-- Returns:
- Result of the CreateRealtimeEndpoint operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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deleteBatchPrediction
DeleteBatchPredictionResult deleteBatchPrediction(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 theBatchPrediction
changed to DELETED.Caution: The result of the
DeleteBatchPrediction
operation is irreversible.- Parameters:
deleteBatchPredictionRequest
-- Returns:
- Result of the DeleteBatchPrediction operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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deleteDataSource
DeleteDataSourceResult deleteDataSource(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 theDataSource
changed to DELETED.Caution: The results of the
DeleteDataSource
operation are irreversible.- Parameters:
deleteDataSourceRequest
-- Returns:
- Result of the DeleteDataSource operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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deleteEvaluation
DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the
DELETED
status to anEvaluation
, rendering it unusable.After invoking the
DeleteEvaluation
operation, you can use the GetEvaluation operation to verify that the status of theEvaluation
changed toDELETED
.Caution: The results of the
DeleteEvaluation
operation are irreversible.- Parameters:
deleteEvaluationRequest
-- Returns:
- Result of the DeleteEvaluation operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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deleteMLModel
DeleteMLModelResult deleteMLModel(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 theMLModel
changed to DELETED.Caution: The result of the
DeleteMLModel
operation is irreversible.- Parameters:
deleteMLModelRequest
-- Returns:
- Result of the DeleteMLModel operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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deleteRealtimeEndpoint
DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel
.- Parameters:
deleteRealtimeEndpointRequest
-- Returns:
- Result of the DeleteRealtimeEndpoint operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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describeBatchPredictions
DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction
operations that match the search criteria in the request.- Parameters:
describeBatchPredictionsRequest
-- Returns:
- Result of the DescribeBatchPredictions operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.
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describeBatchPredictions
DescribeBatchPredictionsResult describeBatchPredictions()
Simplified method form for invoking the DescribeBatchPredictions operation.
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describeDataSources
DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource
that match the search criteria in the request.- Parameters:
describeDataSourcesRequest
-- Returns:
- Result of the DescribeDataSources operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.
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describeDataSources
DescribeDataSourcesResult describeDataSources()
Simplified method form for invoking the DescribeDataSources operation.
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describeEvaluations
DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations
that match the search criteria in the request.- Parameters:
describeEvaluationsRequest
-- Returns:
- Result of the DescribeEvaluations operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.
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describeEvaluations
DescribeEvaluationsResult describeEvaluations()
Simplified method form for invoking the DescribeEvaluations operation.
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describeMLModels
DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of
MLModel
that match the search criteria in the request.- Parameters:
describeMLModelsRequest
-- Returns:
- Result of the DescribeMLModels operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.
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describeMLModels
DescribeMLModelsResult describeMLModels()
Simplified method form for invoking the DescribeMLModels operation.
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getBatchPrediction
GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a
BatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.- Parameters:
getBatchPredictionRequest
-- Returns:
- Result of the GetBatchPrediction operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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getDataSource
GetDataSourceResult getDataSource(GetDataSourceRequest getDataSourceRequest)
Returns a
DataSource
that includes metadata and data file information, as well as the current status of theDataSource
.GetDataSource
provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.- Parameters:
getDataSourceRequest
-- Returns:
- Result of the GetDataSource operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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getEvaluation
GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns an
Evaluation
that includes metadata as well as the current status of theEvaluation
.- Parameters:
getEvaluationRequest
-- Returns:
- Result of the GetEvaluation operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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getMLModel
GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest)
Returns an
MLModel
that includes detailed metadata, and data source information as well as the current status of theMLModel
.GetMLModel
provides results in normal or verbose format.- Parameters:
getMLModelRequest
-- Returns:
- Result of the GetMLModel operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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predict
PredictResult predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model
.Note Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
- Parameters:
predictRequest
-- Returns:
- Result of the Predict operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.LimitExceededException
- The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such asDataSource
.InternalServerException
- An error on the server occurred when trying to process a request.PredictorNotMountedException
- The exception is thrown when a predict request is made to an unmountedMLModel
.
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updateBatchPrediction
UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the
BatchPredictionName
of aBatchPrediction
.You can use the GetBatchPrediction operation to view the contents of the updated data element.
- Parameters:
updateBatchPredictionRequest
-- Returns:
- Result of the UpdateBatchPrediction operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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updateDataSource
UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
Updates the
DataSourceName
of aDataSource
.You can use the GetDataSource operation to view the contents of the updated data element.
- Parameters:
updateDataSourceRequest
-- Returns:
- Result of the UpdateDataSource operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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updateEvaluation
UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
Updates the
EvaluationName
of anEvaluation
.You can use the GetEvaluation operation to view the contents of the updated data element.
- Parameters:
updateEvaluationRequest
-- Returns:
- Result of the UpdateEvaluation operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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updateMLModel
UpdateMLModelResult updateMLModel(UpdateMLModelRequest updateMLModelRequest)
Updates the
MLModelName
and theScoreThreshold
of anMLModel
.You can use the GetMLModel operation to view the contents of the updated data element.
- Parameters:
updateMLModelRequest
-- Returns:
- Result of the UpdateMLModel operation returned by the service.
- Throws:
InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.
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shutdown
void shutdown()
Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
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getCachedResponseMetadata
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
- Parameters:
request
- The originally executed request.- Returns:
- The response metadata for the specified request, or null if none is available.
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