[−][src]Crate rusoto_machinelearning

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

AddTagsInput
AddTagsOutput	Amazon ML returns the following elements.
BatchPrediction	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`.
CreateBatchPredictionInput
CreateBatchPredictionOutput	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.
CreateDataSourceFromRDSInput
CreateDataSourceFromRDSOutput	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.
CreateDataSourceFromRedshiftInput
CreateDataSourceFromRedshiftOutput	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.
CreateDataSourceFromS3Input
CreateDataSourceFromS3Output	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.
CreateEvaluationInput
CreateEvaluationOutput	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.
CreateMLModelInput
CreateMLModelOutput	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.
CreateRealtimeEndpointInput
CreateRealtimeEndpointOutput	Represents the output of an `CreateRealtimeEndpoint` operation. The result contains the `MLModelId` and the endpoint information for the `MLModel`. The endpoint information includes the URI of the `MLModel`; that is, the location to send online prediction requests for the specified `MLModel`.
DataSource	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`.
DeleteBatchPredictionInput
DeleteBatchPredictionOutput	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`.
DeleteDataSourceInput
DeleteDataSourceOutput	Represents the output of a `DeleteDataSource` operation.
DeleteEvaluationInput
DeleteEvaluationOutput	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`.
DeleteMLModelInput
DeleteMLModelOutput	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`.
DeleteRealtimeEndpointInput
DeleteRealtimeEndpointOutput	Represents the output of an `DeleteRealtimeEndpoint` operation. The result contains the `MLModelId` and the endpoint information for the `MLModel`.
DeleteTagsInput
DeleteTagsOutput	Amazon ML returns the following elements.
DescribeBatchPredictionsInput
DescribeBatchPredictionsOutput	Represents the output of a `DescribeBatchPredictions` operation. The content is essentially a list of `BatchPrediction`s.
DescribeDataSourcesInput
DescribeDataSourcesOutput	Represents the query results from a DescribeDataSources operation. The content is essentially a list of `DataSource`.
DescribeEvaluationsInput
DescribeEvaluationsOutput	Represents the query results from a `DescribeEvaluations` operation. The content is essentially a list of `Evaluation`.
DescribeMLModelsInput
DescribeMLModelsOutput	Represents the output of a `DescribeMLModels` operation. The content is essentially a list of `MLModel`.
DescribeTagsInput
DescribeTagsOutput	Amazon ML returns the following elements.
Evaluation	Represents the output of `GetEvaluation` operation. The content consists of the detailed metadata and data file information and the current status of the `Evaluation`.
GetBatchPredictionInput
GetBatchPredictionOutput	Represents the output of a `GetBatchPrediction` operation and describes a `BatchPrediction`.
GetDataSourceInput
GetDataSourceOutput	Represents the output of a `GetDataSource` operation and describes a `DataSource`.
GetEvaluationInput
GetEvaluationOutput	Represents the output of a `GetEvaluation` operation and describes an `Evaluation`.
GetMLModelInput
GetMLModelOutput	Represents the output of a `GetMLModel` operation, and provides detailed information about a `MLModel`.
MLModel	Represents the output of a `GetMLModel` operation. The content consists of the detailed metadata and the current status of the `MLModel`.
MachineLearningClient	A client for the Amazon Machine Learning API.
PerformanceMetrics	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.
PredictInput
PredictOutput
Prediction	The output from a `Predict` operation: `Details` - Contains the following attributes: `DetailsAttributes.PREDICTIVEMODELTYPE - REGRESSION \| BINARY \| MULTICLASS` `DetailsAttributes.ALGORITHM - SGD` `PredictedLabel` - Present for either a `BINARY` or `MULTICLASS` `MLModel` request. `PredictedScores` - Contains the raw classification score corresponding to each label. `PredictedValue` - Present for a `REGRESSION` `MLModel` request.
RDSDataSpec	The data specification of an Amazon Relational Database Service (Amazon RDS) `DataSource`.
RDSDatabase	The database details of an Amazon RDS database.
RDSDatabaseCredentials	The database credentials to connect to a database on an RDS DB instance.
RDSMetadata	The datasource details that are specific to Amazon RDS.
RealtimeEndpointInfo	Describes the real-time endpoint information for an `MLModel`.
RedshiftDataSpec	Describes the data specification of an Amazon Redshift `DataSource`.
RedshiftDatabase	Describes the database details required to connect to an Amazon Redshift database.
RedshiftDatabaseCredentials	Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
RedshiftMetadata	Describes the `DataSource` details specific to Amazon Redshift.
S3DataSpec	Describes the data specification of a `DataSource`.
Tag	A custom key-value pair associated with an ML object, such as an ML model.
UpdateBatchPredictionInput
UpdateBatchPredictionOutput	Represents the output of an `UpdateBatchPrediction` operation. You can see the updated content by using the `GetBatchPrediction` operation.
UpdateDataSourceInput
UpdateDataSourceOutput	Represents the output of an `UpdateDataSource` operation. You can see the updated content by using the `GetBatchPrediction` operation.
UpdateEvaluationInput
UpdateEvaluationOutput	Represents the output of an `UpdateEvaluation` operation. You can see the updated content by using the `GetEvaluation` operation.
UpdateMLModelInput
UpdateMLModelOutput	Represents the output of an `UpdateMLModel` operation. You can see the updated content by using the `GetMLModel` operation.

Enums

AddTagsError	Errors returned by AddTags
CreateBatchPredictionError	Errors returned by CreateBatchPrediction
CreateDataSourceFromRDSError	Errors returned by CreateDataSourceFromRDS
CreateDataSourceFromRedshiftError	Errors returned by CreateDataSourceFromRedshift
CreateDataSourceFromS3Error	Errors returned by CreateDataSourceFromS3
CreateEvaluationError	Errors returned by CreateEvaluation
CreateMLModelError	Errors returned by CreateMLModel
CreateRealtimeEndpointError	Errors returned by CreateRealtimeEndpoint
DeleteBatchPredictionError	Errors returned by DeleteBatchPrediction
DeleteDataSourceError	Errors returned by DeleteDataSource
DeleteEvaluationError	Errors returned by DeleteEvaluation
DeleteMLModelError	Errors returned by DeleteMLModel
DeleteRealtimeEndpointError	Errors returned by DeleteRealtimeEndpoint
DeleteTagsError	Errors returned by DeleteTags
DescribeBatchPredictionsError	Errors returned by DescribeBatchPredictions
DescribeDataSourcesError	Errors returned by DescribeDataSources
DescribeEvaluationsError	Errors returned by DescribeEvaluations
DescribeMLModelsError	Errors returned by DescribeMLModels
DescribeTagsError	Errors returned by DescribeTags
GetBatchPredictionError	Errors returned by GetBatchPrediction
GetDataSourceError	Errors returned by GetDataSource
GetEvaluationError	Errors returned by GetEvaluation
GetMLModelError	Errors returned by GetMLModel
PredictError	Errors returned by Predict
UpdateBatchPredictionError	Errors returned by UpdateBatchPrediction
UpdateDataSourceError	Errors returned by UpdateDataSource
UpdateEvaluationError	Errors returned by UpdateEvaluation
UpdateMLModelError	Errors returned by UpdateMLModel

Traits

MachineLearning

Trait representing the capabilities of the Amazon Machine Learning API. Amazon Machine Learning clients implement this trait.