universal-s3/clients/machinelearning.d.ts

Universal S3 SDK for JavaScript, available for Node.js backends

import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {WaiterConfiguration} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config';
interface Blob {}
declare class MachineLearning extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: MachineLearning.Types.ClientConfiguration)
  config: Config & MachineLearning.Types.ClientConfiguration;
  /**
   * 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.
   */
  addTags(params: MachineLearning.Types.AddTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.AddTagsOutput) => void): Request<MachineLearning.Types.AddTagsOutput, AWSError>;
  /**
   * 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.
   */
  addTags(callback?: (err: AWSError, data: MachineLearning.Types.AddTagsOutput) => void): Request<MachineLearning.Types.AddTagsOutput, AWSError>;
  /**
   * 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.  CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.  You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.
   */
  createBatchPrediction(params: MachineLearning.Types.CreateBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateBatchPredictionOutput) => void): Request<MachineLearning.Types.CreateBatchPredictionOutput, AWSError>;
  /**
   * 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.  CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.  You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.
   */
  createBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.CreateBatchPredictionOutput) => void): Request<MachineLearning.Types.CreateBatchPredictionOutput, AWSError>;
  /**
   * 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. CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform &gt;CreateMLModel&gt;, CreateEvaluation, or CreateBatchPrediction operations.   If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response. 
   */
  createDataSourceFromRDS(params: MachineLearning.Types.CreateDataSourceFromRDSInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRDSOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRDSOutput, AWSError>;
  /**
   * 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. CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform &gt;CreateMLModel&gt;, CreateEvaluation, or CreateBatchPrediction operations.   If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response. 
   */
  createDataSourceFromRDS(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRDSOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRDSOutput, AWSError>;
  /**
   * 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. CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.   If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.  The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation. After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values. 
   */
  createDataSourceFromRedshift(params: MachineLearning.Types.CreateDataSourceFromRedshiftInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRedshiftOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRedshiftOutput, AWSError>;
  /**
   * 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. CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.   If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.  The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation. After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values. 
   */
  createDataSourceFromRedshift(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRedshiftOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRedshiftOutput, AWSError>;
  /**
   * Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.   If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message 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) location, 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 the DataSource.  After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
   */
  createDataSourceFromS3(params: MachineLearning.Types.CreateDataSourceFromS3Input, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromS3Output) => void): Request<MachineLearning.Types.CreateDataSourceFromS3Output, AWSError>;
  /**
   * Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.   If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message 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) location, 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 the DataSource.  After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
   */
  createDataSourceFromS3(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromS3Output) => void): Request<MachineLearning.Types.CreateDataSourceFromS3Output, AWSError>;
  /**
   * 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.  CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.  You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
   */
  createEvaluation(params: MachineLearning.Types.CreateEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateEvaluationOutput) => void): Request<MachineLearning.Types.CreateEvaluationOutput, AWSError>;
  /**
   * 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.  CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.  You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
   */
  createEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.CreateEvaluationOutput) => void): Request<MachineLearning.Types.CreateEvaluationOutput, AWSError>;
  /**
   * Creates a new MLModel using the DataSource and the recipe as information sources.  An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.  CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.  You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.  CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations. 
   */
  createMLModel(params: MachineLearning.Types.CreateMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateMLModelOutput) => void): Request<MachineLearning.Types.CreateMLModelOutput, AWSError>;
  /**
   * Creates a new MLModel using the DataSource and the recipe as information sources.  An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.  CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.  You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.  CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations. 
   */
  createMLModel(callback?: (err: AWSError, data: MachineLearning.Types.CreateMLModelOutput) => void): Request<MachineLearning.Types.CreateMLModelOutput, AWSError>;
  /**
   * 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.
   */
  createRealtimeEndpoint(params: MachineLearning.Types.CreateRealtimeEndpointInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateRealtimeEndpointOutput) => void): Request<MachineLearning.Types.CreateRealtimeEndpointOutput, AWSError>;
  /**
   * 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.
   */
  createRealtimeEndpoint(callback?: (err: AWSError, data: MachineLearning.Types.CreateRealtimeEndpointOutput) => void): Request<MachineLearning.Types.CreateRealtimeEndpointOutput, AWSError>;
  /**
   * 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 the BatchPrediction changed to DELETED. Caution: The result of the DeleteBatchPrediction operation is irreversible.
   */
  deleteBatchPrediction(params: MachineLearning.Types.DeleteBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteBatchPredictionOutput) => void): Request<MachineLearning.Types.DeleteBatchPredictionOutput, AWSError>;
  /**
   * 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 the BatchPrediction changed to DELETED. Caution: The result of the DeleteBatchPrediction operation is irreversible.
   */
  deleteBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.DeleteBatchPredictionOutput) => void): Request<MachineLearning.Types.DeleteBatchPredictionOutput, AWSError>;
  /**
   * 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 the DataSource changed to DELETED. Caution: The results of the DeleteDataSource operation are irreversible.
   */
  deleteDataSource(params: MachineLearning.Types.DeleteDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteDataSourceOutput) => void): Request<MachineLearning.Types.DeleteDataSourceOutput, AWSError>;
  /**
   * 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 the DataSource changed to DELETED. Caution: The results of the DeleteDataSource operation are irreversible.
   */
  deleteDataSource(callback?: (err: AWSError, data: MachineLearning.Types.DeleteDataSourceOutput) => void): Request<MachineLearning.Types.DeleteDataSourceOutput, AWSError>;
  /**
   * Assigns the DELETED status to an Evaluation, rendering it unusable. After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED. Caution The results of the DeleteEvaluation operation are irreversible.
   */
  deleteEvaluation(params: MachineLearning.Types.DeleteEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteEvaluationOutput) => void): Request<MachineLearning.Types.DeleteEvaluationOutput, AWSError>;
  /**
   * Assigns the DELETED status to an Evaluation, rendering it unusable. After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED. Caution The results of the DeleteEvaluation operation are irreversible.
   */
  deleteEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.DeleteEvaluationOutput) => void): Request<MachineLearning.Types.DeleteEvaluationOutput, AWSError>;
  /**
   * 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 the MLModel changed to DELETED. Caution: The result of the DeleteMLModel operation is irreversible.
   */
  deleteMLModel(params: MachineLearning.Types.DeleteMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteMLModelOutput) => void): Request<MachineLearning.Types.DeleteMLModelOutput, AWSError>;
  /**
   * 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 the MLModel changed to DELETED. Caution: The result of the DeleteMLModel operation is irreversible.
   */
  deleteMLModel(callback?: (err: AWSError, data: MachineLearning.Types.DeleteMLModelOutput) => void): Request<MachineLearning.Types.DeleteMLModelOutput, AWSError>;
  /**
   * Deletes a real time endpoint of an MLModel.
   */
  deleteRealtimeEndpoint(params: MachineLearning.Types.DeleteRealtimeEndpointInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteRealtimeEndpointOutput) => void): Request<MachineLearning.Types.DeleteRealtimeEndpointOutput, AWSError>;
  /**
   * Deletes a real time endpoint of an MLModel.
   */
  deleteRealtimeEndpoint(callback?: (err: AWSError, data: MachineLearning.Types.DeleteRealtimeEndpointOutput) => void): Request<MachineLearning.Types.DeleteRealtimeEndpointOutput, AWSError>;
  /**
   * Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags. If you specify a tag that doesn't exist, Amazon ML ignores it.
   */
  deleteTags(params: MachineLearning.Types.DeleteTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteTagsOutput) => void): Request<MachineLearning.Types.DeleteTagsOutput, AWSError>;
  /**
   * Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags. If you specify a tag that doesn't exist, Amazon ML ignores it.
   */
  deleteTags(callback?: (err: AWSError, data: MachineLearning.Types.DeleteTagsOutput) => void): Request<MachineLearning.Types.DeleteTagsOutput, AWSError>;
  /**
   * Returns a list of BatchPrediction operations that match the search criteria in the request.
   */
  describeBatchPredictions(params: MachineLearning.Types.DescribeBatchPredictionsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
  /**
   * Returns a list of BatchPrediction operations that match the search criteria in the request.
   */
  describeBatchPredictions(callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
  /**
   * Returns a list of DataSource that match the search criteria in the request.
   */
  describeDataSources(params: MachineLearning.Types.DescribeDataSourcesInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
  /**
   * Returns a list of DataSource that match the search criteria in the request.
   */
  describeDataSources(callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
  /**
   * Returns a list of DescribeEvaluations that match the search criteria in the request.
   */
  describeEvaluations(params: MachineLearning.Types.DescribeEvaluationsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
  /**
   * Returns a list of DescribeEvaluations that match the search criteria in the request.
   */
  describeEvaluations(callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
  /**
   * Returns a list of MLModel that match the search criteria in the request.
   */
  describeMLModels(params: MachineLearning.Types.DescribeMLModelsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
  /**
   * Returns a list of MLModel that match the search criteria in the request.
   */
  describeMLModels(callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
  /**
   * Describes one or more of the tags for your Amazon ML object.
   */
  describeTags(params: MachineLearning.Types.DescribeTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeTagsOutput) => void): Request<MachineLearning.Types.DescribeTagsOutput, AWSError>;
  /**
   * Describes one or more of the tags for your Amazon ML object.
   */
  describeTags(callback?: (err: AWSError, data: MachineLearning.Types.DescribeTagsOutput) => void): Request<MachineLearning.Types.DescribeTagsOutput, AWSError>;
  /**
   * Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
   */
  getBatchPrediction(params: MachineLearning.Types.GetBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.GetBatchPredictionOutput) => void): Request<MachineLearning.Types.GetBatchPredictionOutput, AWSError>;
  /**
   * Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
   */
  getBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.GetBatchPredictionOutput) => void): Request<MachineLearning.Types.GetBatchPredictionOutput, AWSError>;
  /**
   * Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource. 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.
   */
  getDataSource(params: MachineLearning.Types.GetDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.GetDataSourceOutput) => void): Request<MachineLearning.Types.GetDataSourceOutput, AWSError>;
  /**
   * Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource. 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.
   */
  getDataSource(callback?: (err: AWSError, data: MachineLearning.Types.GetDataSourceOutput) => void): Request<MachineLearning.Types.GetDataSourceOutput, AWSError>;
  /**
   * Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
   */
  getEvaluation(params: MachineLearning.Types.GetEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.GetEvaluationOutput) => void): Request<MachineLearning.Types.GetEvaluationOutput, AWSError>;
  /**
   * Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
   */
  getEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.GetEvaluationOutput) => void): Request<MachineLearning.Types.GetEvaluationOutput, AWSError>;
  /**
   * Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel. GetMLModel provides results in normal or verbose format. 
   */
  getMLModel(params: MachineLearning.Types.GetMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.GetMLModelOutput) => void): Request<MachineLearning.Types.GetMLModelOutput, AWSError>;
  /**
   * Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel. GetMLModel provides results in normal or verbose format. 
   */
  getMLModel(callback?: (err: AWSError, data: MachineLearning.Types.GetMLModelOutput) => void): Request<MachineLearning.Types.GetMLModelOutput, AWSError>;
  /**
   * 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.
   */
  predict(params: MachineLearning.Types.PredictInput, callback?: (err: AWSError, data: MachineLearning.Types.PredictOutput) => void): Request<MachineLearning.Types.PredictOutput, AWSError>;
  /**
   * 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.
   */
  predict(callback?: (err: AWSError, data: MachineLearning.Types.PredictOutput) => void): Request<MachineLearning.Types.PredictOutput, AWSError>;
  /**
   * Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element.
   */
  updateBatchPrediction(params: MachineLearning.Types.UpdateBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateBatchPredictionOutput) => void): Request<MachineLearning.Types.UpdateBatchPredictionOutput, AWSError>;
  /**
   * Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element.
   */
  updateBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.UpdateBatchPredictionOutput) => void): Request<MachineLearning.Types.UpdateBatchPredictionOutput, AWSError>;
  /**
   * Updates the DataSourceName of a DataSource. You can use the GetDataSource operation to view the contents of the updated data element.
   */
  updateDataSource(params: MachineLearning.Types.UpdateDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateDataSourceOutput) => void): Request<MachineLearning.Types.UpdateDataSourceOutput, AWSError>;
  /**
   * Updates the DataSourceName of a DataSource. You can use the GetDataSource operation to view the contents of the updated data element.
   */
  updateDataSource(callback?: (err: AWSError, data: MachineLearning.Types.UpdateDataSourceOutput) => void): Request<MachineLearning.Types.UpdateDataSourceOutput, AWSError>;
  /**
   * Updates the EvaluationName of an Evaluation. You can use the GetEvaluation operation to view the contents of the updated data element.
   */
  updateEvaluation(params: MachineLearning.Types.UpdateEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateEvaluationOutput) => void): Request<MachineLearning.Types.UpdateEvaluationOutput, AWSError>;
  /**
   * Updates the EvaluationName of an Evaluation. You can use the GetEvaluation operation to view the contents of the updated data element.
   */
  updateEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.UpdateEvaluationOutput) => void): Request<MachineLearning.Types.UpdateEvaluationOutput, AWSError>;
  /**
   * Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element.
   */
  updateMLModel(params: MachineLearning.Types.UpdateMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateMLModelOutput) => void): Request<MachineLearning.Types.UpdateMLModelOutput, AWSError>;
  /**
   * Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element.
   */
  updateMLModel(callback?: (err: AWSError, data: MachineLearning.Types.UpdateMLModelOutput) => void): Request<MachineLearning.Types.UpdateMLModelOutput, AWSError>;
  /**
   * Waits for the dataSourceAvailable state by periodically calling the underlying MachineLearning.describeDataSourcesoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "dataSourceAvailable", params: MachineLearning.Types.DescribeDataSourcesInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
  /**
   * Waits for the dataSourceAvailable state by periodically calling the underlying MachineLearning.describeDataSourcesoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "dataSourceAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
  /**
   * Waits for the mLModelAvailable state by periodically calling the underlying MachineLearning.describeMLModelsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "mLModelAvailable", params: MachineLearning.Types.DescribeMLModelsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
  /**
   * Waits for the mLModelAvailable state by periodically calling the underlying MachineLearning.describeMLModelsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "mLModelAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
  /**
   * Waits for the evaluationAvailable state by periodically calling the underlying MachineLearning.describeEvaluationsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "evaluationAvailable", params: MachineLearning.Types.DescribeEvaluationsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
  /**
   * Waits for the evaluationAvailable state by periodically calling the underlying MachineLearning.describeEvaluationsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "evaluationAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
  /**
   * Waits for the batchPredictionAvailable state by periodically calling the underlying MachineLearning.describeBatchPredictionsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "batchPredictionAvailable", params: MachineLearning.Types.DescribeBatchPredictionsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
  /**
   * Waits for the batchPredictionAvailable state by periodically calling the underlying MachineLearning.describeBatchPredictionsoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "batchPredictionAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
}
declare namespace MachineLearning {
  export interface AddTagsInput {
    /**
     * The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.
     */
    Tags: TagList;
    /**
     * The ID of the ML object to tag. For example, exampleModelId.
     */
    ResourceId: EntityId;
    /**
     * The type of the ML object to tag. 
     */
    ResourceType: TaggableResourceType;
  }
  export interface AddTagsOutput {
    /**
     * The ID of the ML object that was tagged.
     */
    ResourceId?: EntityId;
    /**
     * The type of the ML object that was tagged.
     */
    ResourceType?: TaggableResourceType;
  }
  export type Algorithm = "sgd"|string;
  export type AwsUserArn = string;
  export interface BatchPrediction {
    /**
     * The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. 
     */
    BatchPredictionId?: EntityId;
    /**
     * The ID of the MLModel that generated predictions for the BatchPrediction request.
     */
    MLModelId?: EntityId;
    /**
     * The ID of the DataSource that points to the group of observations to predict.
     */
    BatchPredictionDataSourceId?: EntityId;
    /**
     * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
     */
    InputDataLocationS3?: S3Url;
    /**
     * The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
     */
    CreatedByIamUser?: AwsUserArn;
    /**
     * The time that the BatchPrediction was created. The time is expressed in epoch time.
     */
    CreatedAt?: EpochTime;
    /**
     * The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.
     */
    LastUpdatedAt?: EpochTime;
    /**
     * A user-supplied name or description of the BatchPrediction.
     */
    Name?: EntityName;
    /**
     * The status of the BatchPrediction. This element can have one of the following values:   PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.  INPROGRESS - The process is underway.  FAILED - The request to perform a batch prediction did not run to completion. It is not usable.  COMPLETED - The batch prediction process completed successfully.  DELETED - The BatchPrediction is marked as deleted. It is not usable. 
     */
    Status?: EntityStatus;
    /**
     * The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.
     */
    OutputUri?: S3Url;
    /**
     * A description of the most recent details about processing the batch prediction request.
     */
    Message?: Message;
    ComputeTime?: LongType;
    FinishedAt?: EpochTime;
    StartedAt?: EpochTime;
    TotalRecordCount?: LongType;
    InvalidRecordCount?: LongType;
  }
  export type BatchPredictionFilterVariable = "CreatedAt"|"LastUpdatedAt"|"Status"|"Name"|"IAMUser"|"MLModelId"|"DataSourceId"|"DataURI"|string;
  export type BatchPredictions = BatchPrediction[];
  export type ComparatorValue = string;
  export type ComputeStatistics = boolean;
  export interface CreateBatchPredictionInput {
    /**
     * A user-supplied ID that uniquely identifies the BatchPrediction.
     */
    BatchPredictionId: EntityId;
    /**
     * A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.
     */
    BatchPredictionName?: EntityName;
    /**
     * The ID of the MLModel that will generate predictions for the group of observations. 
     */
    MLModelId: EntityId;
    /**
     * The ID of the DataSource that points to the group of observations to predict.
     */
    BatchPredictionDataSourceId: EntityId;
    /**
     * The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'. Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.
     */
    OutputUri: S3Url;
  }
  export interface CreateBatchPredictionOutput {
    /**
     * A user-supplied ID that uniquely identifies the BatchPrediction. This value is identical to the value of the BatchPredictionId in the request.
     */
    BatchPredictionId?: EntityId;
  }
  export interface CreateDataSourceFromRDSInput {
    /**
     * A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.
     */
    DataSourceId: EntityId;
    /**
     * A user-supplied name or description of the DataSource.
     */
    DataSourceName?: EntityName;
    /**
     * The data specification of an Amazon RDS DataSource:  DatabaseInformation -   DatabaseName - The name of the Amazon RDS database.  InstanceIdentifier  - A unique identifier for the Amazon RDS database instance.   DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database. ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines. ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines. SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds] pair for a VPC-based RDS DB instance. SelectSqlQuery - A query that is used to retrieve the observation data for the Datasource. S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location. DataSchemaUri - The Amazon S3 location of the DataSchema. DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.    Sample -  "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"   
     */
    RDSData: RDSDataSpec;
    /**
     * The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery query from Amazon RDS to Amazon S3.  
     */
    RoleARN: RoleARN;
    /**
     * The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training. 
     */
    ComputeStatistics?: ComputeStatistics;
  }
  export interface CreateDataSourceFromRDSOutput {
    /**
     * A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. 
     */
    DataSourceId?: EntityId;
  }
  export interface CreateDataSourceFromRedshiftInput {
    /**
     * A user-supplied ID that uniquely identifies the DataSource.
     */
    DataSourceId: EntityId;
    /**
     * A user-supplied name or description of the DataSource. 
     */
    DataSourceName?: EntityName;
    /**
     * The data specification of an Amazon Redshift DataSource:  DatabaseInformation -   DatabaseName - The name of the Amazon Redshift database.    ClusterIdentifier - The unique ID for the Amazon Redshift cluster.  DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database. SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource. S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location. DataSchemaUri - The Amazon S3 location of the DataSchema. DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource.  Sample -  "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"   
     */
    DataSpec: RedshiftDataSpec;
    /**
     * A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:    A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation  
     */
    RoleARN: RoleARN;
    /**
     * The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.
     */
    ComputeStatistics?: ComputeStatistics;
  }
  export interface CreateDataSourceFromRedshiftOutput {
    /**
     * A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. 
     */
    DataSourceId?: EntityId;
  }
  export interface CreateDataSourceFromS3Input {
    /**
     * A user-supplied identifier that uniquely identifies the DataSource. 
     */
    DataSourceId: EntityId;
    /**
     * A user-supplied name or description of the DataSource. 
     */
    DataSourceName?: EntityName;
    /**
     * The data specification of a DataSource:  DataLocationS3 - The Amazon S3 location of the observation data. DataSchemaLocationS3 - The Amazon S3 location of the DataSchema. DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.   Sample -  "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"   
     */
    DataSpec: S3DataSpec;
    /**
     * The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.
     */
    ComputeStatistics?: ComputeStatistics;
  }
  export interface CreateDataSourceFromS3Output {
    /**
     * A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request. 
     */
    DataSourceId?: EntityId;
  }
  export interface CreateEvaluationInput {
    /**
     * A user-supplied ID that uniquely identifies the Evaluation.
     */
    EvaluationId: EntityId;
    /**
     * A user-supplied name or description of the Evaluation.
     */
    EvaluationName?: EntityName;
    /**
     * The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.
     */
    MLModelId: EntityId;
    /**
     * The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.
     */
    EvaluationDataSourceId: EntityId;
  }
  export interface CreateEvaluationOutput {
    /**
     * The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the Evaluat