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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 BatchPrediction
s.
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
MLModel
uses the Area Under the Curve (AUC) technique to measure performance. -
RegressionRMSE: The regression
MLModel
uses 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
MLModel
uses 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 | MULTICLASS
DetailsAttributes.ALGORITHM - SGD
-
PredictedLabel
- Present for either aBINARY
orMULTICLASS
MLModel
request. -
PredictedScores
- Contains the raw classification score corresponding to each label. -
PredictedValue
- Present for aREGRESSION
MLModel
request.
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.