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Definition of the public APIs exposed by Amazon Machine Learning
If you’re using the service, you’re probably looking for MachineLearningClient and MachineLearning.
Structs
Amazon ML returns the following elements.
Represents the output of a GetBatchPrediction operation.
The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.
Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.
Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.
Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
Note: The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.
Represents the output of the GetDataSource operation.
The content consists of the detailed metadata and data file information and the current status of the DataSource.
Represents the output of a DeleteBatchPrediction operation.
You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.
Represents the output of a DeleteDataSource operation.
Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.
Represents the output of a DeleteMLModel operation.
You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
Amazon ML returns the following elements.
Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.
Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.
Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.
Amazon ML returns the following elements.
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation.
Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.
Represents the output of a GetDataSource operation and describes a DataSource.
Represents the output of a GetEvaluation operation and describes an Evaluation.
Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.
Represents the output of a GetMLModel operation.
The content consists of the detailed metadata and the current status of the MLModel.
A client for the Amazon Machine Learning API.
Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:
-
BinaryAUC: The binary
MLModeluses the Area Under the Curve (AUC) technique to measure performance. -
RegressionRMSE: The regression
MLModeluses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. -
MulticlassAvgFScore: The multiclass
MLModeluses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
The output from a Predict operation:
-
Details- Contains the following attributes:DetailsAttributes.PREDICTIVEMODELTYPE - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD -
PredictedLabel- Present for either aBINARYorMULTICLASSMLModelrequest. -
PredictedScores- Contains the raw classification score corresponding to each label. -
PredictedValue- Present for aREGRESSIONMLModelrequest.
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
The database details of an Amazon RDS database.
The database credentials to connect to a database on an RDS DB instance.
The datasource details that are specific to Amazon RDS.
Describes the real-time endpoint information for an MLModel.
Describes the data specification of an Amazon Redshift DataSource.
Describes the database details required to connect to an Amazon Redshift database.
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
Describes the DataSource details specific to Amazon Redshift.
Describes the data specification of a DataSource.
A custom key-value pair associated with an ML object, such as an ML model.
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
Enums
Errors returned by AddTags
Errors returned by CreateBatchPrediction
Errors returned by CreateDataSourceFromRDS
Errors returned by CreateDataSourceFromRedshift
Errors returned by CreateDataSourceFromS3
Errors returned by CreateEvaluation
Errors returned by CreateMLModel
Errors returned by CreateRealtimeEndpoint
Errors returned by DeleteBatchPrediction
Errors returned by DeleteDataSource
Errors returned by DeleteEvaluation
Errors returned by DeleteMLModel
Errors returned by DeleteRealtimeEndpoint
Errors returned by DeleteTags
Errors returned by DescribeBatchPredictions
Errors returned by DescribeDataSources
Errors returned by DescribeEvaluations
Errors returned by DescribeMLModels
Errors returned by DescribeTags
Errors returned by GetBatchPrediction
Errors returned by GetDataSource
Errors returned by GetEvaluation
Errors returned by GetMLModel
Errors returned by Predict
Errors returned by UpdateBatchPrediction
Errors returned by UpdateDataSource
Errors returned by UpdateEvaluation
Errors returned by UpdateMLModel
Traits
Trait representing the capabilities of the Amazon Machine Learning API. Amazon Machine Learning clients implement this trait.