MachineLearning class

Definition of the public APIs exposed by Amazon Machine Learning

Constructors

MachineLearning({required String region, AwsClientCredentials? credentials, AwsClientCredentialsProvider? credentialsProvider, Client? client, String? endpointUrl})

Properties

hashCode → int
The hash code for this object.
no setterinherited
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

addTags({required String resourceId, required TaggableResourceType resourceType, required List<Tag> tags}) → Future<AddTagsOutput>
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.
close() → void
Closes the internal HTTP client if none was provided at creation. If a client was passed as a constructor argument, this becomes a noop.
createBatchPrediction({required String batchPredictionDataSourceId, required String batchPredictionId, required String mLModelId, required String outputUri, String? batchPredictionName}) → Future<CreateBatchPredictionOutput>
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.
createDataSourceFromRDS({required String dataSourceId, required RDSDataSpec rDSData, required String roleARN, bool? computeStatistics, String? dataSourceName}) → Future<CreateDataSourceFromRDSOutput>
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.
createDataSourceFromRedshift({required String dataSourceId, required RedshiftDataSpec dataSpec, required String roleARN, bool? computeStatistics, String? dataSourceName}) → Future<CreateDataSourceFromRedshiftOutput>
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.
createDataSourceFromS3({required String dataSourceId, required S3DataSpec dataSpec, bool? computeStatistics, String? dataSourceName}) → Future<CreateDataSourceFromS3Output>
Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
createEvaluation({required String evaluationDataSourceId, required String evaluationId, required String mLModelId, String? evaluationName}) → Future<CreateEvaluationOutput>
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.
createMLModel({required String mLModelId, required MLModelType mLModelType, required String trainingDataSourceId, String? mLModelName, Map<String, String>? parameters, String? recipe, String? recipeUri}) → Future<CreateMLModelOutput>
Creates a new MLModel using the DataSource and the recipe as information sources.
createRealtimeEndpoint({required String mLModelId}) → Future<CreateRealtimeEndpointOutput>
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.
deleteBatchPrediction({required String batchPredictionId}) → Future<DeleteBatchPredictionOutput>
Assigns the DELETED status to a BatchPrediction, rendering it unusable.
deleteDataSource({required String dataSourceId}) → Future<DeleteDataSourceOutput>
Assigns the DELETED status to a DataSource, rendering it unusable.
deleteEvaluation({required String evaluationId}) → Future<DeleteEvaluationOutput>
Assigns the DELETED status to an Evaluation, rendering it unusable.
deleteMLModel({required String mLModelId}) → Future<DeleteMLModelOutput>
Assigns the DELETED status to an MLModel, rendering it unusable.
deleteRealtimeEndpoint({required String mLModelId}) → Future<DeleteRealtimeEndpointOutput>
Deletes a real time endpoint of an MLModel.
deleteTags({required String resourceId, required TaggableResourceType resourceType, required List<String> tagKeys}) → Future<DeleteTagsOutput>
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
describeBatchPredictions({String? eq, BatchPredictionFilterVariable? filterVariable, String? ge, String? gt, String? le, String? lt, int? limit, String? ne, String? nextToken, String? prefix, SortOrder? sortOrder}) → Future<DescribeBatchPredictionsOutput>
Returns a list of BatchPrediction operations that match the search criteria in the request.
describeDataSources({String? eq, DataSourceFilterVariable? filterVariable, String? ge, String? gt, String? le, String? lt, int? limit, String? ne, String? nextToken, String? prefix, SortOrder? sortOrder}) → Future<DescribeDataSourcesOutput>
Returns a list of DataSource that match the search criteria in the request.
describeEvaluations({String? eq, EvaluationFilterVariable? filterVariable, String? ge, String? gt, String? le, String? lt, int? limit, String? ne, String? nextToken, String? prefix, SortOrder? sortOrder}) → Future<DescribeEvaluationsOutput>
Returns a list of DescribeEvaluations that match the search criteria in the request.
describeMLModels({String? eq, MLModelFilterVariable? filterVariable, String? ge, String? gt, String? le, String? lt, int? limit, String? ne, String? nextToken, String? prefix, SortOrder? sortOrder}) → Future<DescribeMLModelsOutput>
Returns a list of MLModel that match the search criteria in the request.
describeTags({required String resourceId, required TaggableResourceType resourceType}) → Future<DescribeTagsOutput>
Describes one or more of the tags for your Amazon ML object.
getBatchPrediction({required String batchPredictionId}) → Future<GetBatchPredictionOutput>
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
getDataSource({required String dataSourceId, bool? verbose}) → Future<GetDataSourceOutput>
Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
getEvaluation({required String evaluationId}) → Future<GetEvaluationOutput>
Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
getMLModel({required String mLModelId, bool? verbose}) → Future<GetMLModelOutput>
Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
predict({required String mLModelId, required String predictEndpoint, required Map<String, String> record}) → Future<PredictOutput>
Generates a prediction for the observation using the specified ML Model.
toString() → String
A string representation of this object.
inherited
updateBatchPrediction({required String batchPredictionId, required String batchPredictionName}) → Future<UpdateBatchPredictionOutput>
Updates the BatchPredictionName of a BatchPrediction.
updateDataSource({required String dataSourceId, required String dataSourceName}) → Future<UpdateDataSourceOutput>
Updates the DataSourceName of a DataSource.
updateEvaluation({required String evaluationId, required String evaluationName}) → Future<UpdateEvaluationOutput>
Updates the EvaluationName of an Evaluation.
updateMLModel({required String mLModelId, String? mLModelName, double? scoreThreshold}) → Future<UpdateMLModelOutput>
Updates the MLModelName and the ScoreThreshold of an MLModel.

Operators

operator ==(Object other) → bool
The equality operator.
inherited