1  import {Request} from '../lib/request';
   2  import {Response} from '../lib/response';
   3  import {AWSError} from '../lib/error';
   4  import {Service} from '../lib/service';
   5  import {WaiterConfiguration} from '../lib/service';
   6  import {ServiceConfigurationOptions} from '../lib/service';
   7  import {ConfigBase as Config} from '../lib/config-base';
   8  interface Blob {}
   9  declare class MachineLearning extends Service {
  10    /**
  11     * Constructs a service object. This object has one method for each API operation.
  12     */
  13    constructor(options?: MachineLearning.Types.ClientConfiguration)
  14    config: Config & MachineLearning.Types.ClientConfiguration;
  15    /**
  16     * 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.
  17     */
  18    addTags(params: MachineLearning.Types.AddTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.AddTagsOutput) => void): Request<MachineLearning.Types.AddTagsOutput, AWSError>;
  19    /**
  20     * 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.
  21     */
  22    addTags(callback?: (err: AWSError, data: MachineLearning.Types.AddTagsOutput) => void): Request<MachineLearning.Types.AddTagsOutput, AWSError>;
  23    /**
  24     * 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.
  25     */
  26    createBatchPrediction(params: MachineLearning.Types.CreateBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateBatchPredictionOutput) => void): Request<MachineLearning.Types.CreateBatchPredictionOutput, AWSError>;
  27    /**
  28     * 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.
  29     */
  30    createBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.CreateBatchPredictionOutput) => void): Request<MachineLearning.Types.CreateBatchPredictionOutput, AWSError>;
  31    /**
  32     * 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. 
  33     */
  34    createDataSourceFromRDS(params: MachineLearning.Types.CreateDataSourceFromRDSInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRDSOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRDSOutput, AWSError>;
  35    /**
  36     * 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. 
  37     */
  38    createDataSourceFromRDS(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRDSOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRDSOutput, AWSError>;
  39    /**
  40     * 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.
  41     */
  42    createDataSourceFromRedshift(params: MachineLearning.Types.CreateDataSourceFromRedshiftInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRedshiftOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRedshiftOutput, AWSError>;
  43    /**
  44     * 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.
  45     */
  46    createDataSourceFromRedshift(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromRedshiftOutput) => void): Request<MachineLearning.Types.CreateDataSourceFromRedshiftOutput, AWSError>;
  47    /**
  48     * 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.
  49     */
  50    createDataSourceFromS3(params: MachineLearning.Types.CreateDataSourceFromS3Input, callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromS3Output) => void): Request<MachineLearning.Types.CreateDataSourceFromS3Output, AWSError>;
  51    /**
  52     * 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.
  53     */
  54    createDataSourceFromS3(callback?: (err: AWSError, data: MachineLearning.Types.CreateDataSourceFromS3Output) => void): Request<MachineLearning.Types.CreateDataSourceFromS3Output, AWSError>;
  55    /**
  56     * 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.
  57     */
  58    createEvaluation(params: MachineLearning.Types.CreateEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateEvaluationOutput) => void): Request<MachineLearning.Types.CreateEvaluationOutput, AWSError>;
  59    /**
  60     * 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.
  61     */
  62    createEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.CreateEvaluationOutput) => void): Request<MachineLearning.Types.CreateEvaluationOutput, AWSError>;
  63    /**
  64     * 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. 
  65     */
  66    createMLModel(params: MachineLearning.Types.CreateMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateMLModelOutput) => void): Request<MachineLearning.Types.CreateMLModelOutput, AWSError>;
  67    /**
  68     * 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. 
  69     */
  70    createMLModel(callback?: (err: AWSError, data: MachineLearning.Types.CreateMLModelOutput) => void): Request<MachineLearning.Types.CreateMLModelOutput, AWSError>;
  71    /**
  72     * 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.
  73     */
  74    createRealtimeEndpoint(params: MachineLearning.Types.CreateRealtimeEndpointInput, callback?: (err: AWSError, data: MachineLearning.Types.CreateRealtimeEndpointOutput) => void): Request<MachineLearning.Types.CreateRealtimeEndpointOutput, AWSError>;
  75    /**
  76     * 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.
  77     */
  78    createRealtimeEndpoint(callback?: (err: AWSError, data: MachineLearning.Types.CreateRealtimeEndpointOutput) => void): Request<MachineLearning.Types.CreateRealtimeEndpointOutput, AWSError>;
  79    /**
  80     * 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.
  81     */
  82    deleteBatchPrediction(params: MachineLearning.Types.DeleteBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteBatchPredictionOutput) => void): Request<MachineLearning.Types.DeleteBatchPredictionOutput, AWSError>;
  83    /**
  84     * 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.
  85     */
  86    deleteBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.DeleteBatchPredictionOutput) => void): Request<MachineLearning.Types.DeleteBatchPredictionOutput, AWSError>;
  87    /**
  88     * 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.
  89     */
  90    deleteDataSource(params: MachineLearning.Types.DeleteDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteDataSourceOutput) => void): Request<MachineLearning.Types.DeleteDataSourceOutput, AWSError>;
  91    /**
  92     * 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.
  93     */
  94    deleteDataSource(callback?: (err: AWSError, data: MachineLearning.Types.DeleteDataSourceOutput) => void): Request<MachineLearning.Types.DeleteDataSourceOutput, AWSError>;
  95    /**
  96     * 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.
  97     */
  98    deleteEvaluation(params: MachineLearning.Types.DeleteEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteEvaluationOutput) => void): Request<MachineLearning.Types.DeleteEvaluationOutput, AWSError>;
  99    /**
 100     * 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.
 101     */
 102    deleteEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.DeleteEvaluationOutput) => void): Request<MachineLearning.Types.DeleteEvaluationOutput, AWSError>;
 103    /**
 104     * 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.
 105     */
 106    deleteMLModel(params: MachineLearning.Types.DeleteMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteMLModelOutput) => void): Request<MachineLearning.Types.DeleteMLModelOutput, AWSError>;
 107    /**
 108     * 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.
 109     */
 110    deleteMLModel(callback?: (err: AWSError, data: MachineLearning.Types.DeleteMLModelOutput) => void): Request<MachineLearning.Types.DeleteMLModelOutput, AWSError>;
 111    /**
 112     * Deletes a real time endpoint of an MLModel.
 113     */
 114    deleteRealtimeEndpoint(params: MachineLearning.Types.DeleteRealtimeEndpointInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteRealtimeEndpointOutput) => void): Request<MachineLearning.Types.DeleteRealtimeEndpointOutput, AWSError>;
 115    /**
 116     * Deletes a real time endpoint of an MLModel.
 117     */
 118    deleteRealtimeEndpoint(callback?: (err: AWSError, data: MachineLearning.Types.DeleteRealtimeEndpointOutput) => void): Request<MachineLearning.Types.DeleteRealtimeEndpointOutput, AWSError>;
 119    /**
 120     * 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.
 121     */
 122    deleteTags(params: MachineLearning.Types.DeleteTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.DeleteTagsOutput) => void): Request<MachineLearning.Types.DeleteTagsOutput, AWSError>;
 123    /**
 124     * 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.
 125     */
 126    deleteTags(callback?: (err: AWSError, data: MachineLearning.Types.DeleteTagsOutput) => void): Request<MachineLearning.Types.DeleteTagsOutput, AWSError>;
 127    /**
 128     * Returns a list of BatchPrediction operations that match the search criteria in the request.
 129     */
 130    describeBatchPredictions(params: MachineLearning.Types.DescribeBatchPredictionsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
 131    /**
 132     * Returns a list of BatchPrediction operations that match the search criteria in the request.
 133     */
 134    describeBatchPredictions(callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
 135    /**
 136     * Returns a list of DataSource that match the search criteria in the request.
 137     */
 138    describeDataSources(params: MachineLearning.Types.DescribeDataSourcesInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
 139    /**
 140     * Returns a list of DataSource that match the search criteria in the request.
 141     */
 142    describeDataSources(callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
 143    /**
 144     * Returns a list of DescribeEvaluations that match the search criteria in the request.
 145     */
 146    describeEvaluations(params: MachineLearning.Types.DescribeEvaluationsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
 147    /**
 148     * Returns a list of DescribeEvaluations that match the search criteria in the request.
 149     */
 150    describeEvaluations(callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
 151    /**
 152     * Returns a list of MLModel that match the search criteria in the request.
 153     */
 154    describeMLModels(params: MachineLearning.Types.DescribeMLModelsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
 155    /**
 156     * Returns a list of MLModel that match the search criteria in the request.
 157     */
 158    describeMLModels(callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
 159    /**
 160     * Describes one or more of the tags for your Amazon ML object.
 161     */
 162    describeTags(params: MachineLearning.Types.DescribeTagsInput, callback?: (err: AWSError, data: MachineLearning.Types.DescribeTagsOutput) => void): Request<MachineLearning.Types.DescribeTagsOutput, AWSError>;
 163    /**
 164     * Describes one or more of the tags for your Amazon ML object.
 165     */
 166    describeTags(callback?: (err: AWSError, data: MachineLearning.Types.DescribeTagsOutput) => void): Request<MachineLearning.Types.DescribeTagsOutput, AWSError>;
 167    /**
 168     * Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
 169     */
 170    getBatchPrediction(params: MachineLearning.Types.GetBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.GetBatchPredictionOutput) => void): Request<MachineLearning.Types.GetBatchPredictionOutput, AWSError>;
 171    /**
 172     * Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
 173     */
 174    getBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.GetBatchPredictionOutput) => void): Request<MachineLearning.Types.GetBatchPredictionOutput, AWSError>;
 175    /**
 176     * 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.
 177     */
 178    getDataSource(params: MachineLearning.Types.GetDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.GetDataSourceOutput) => void): Request<MachineLearning.Types.GetDataSourceOutput, AWSError>;
 179    /**
 180     * 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.
 181     */
 182    getDataSource(callback?: (err: AWSError, data: MachineLearning.Types.GetDataSourceOutput) => void): Request<MachineLearning.Types.GetDataSourceOutput, AWSError>;
 183    /**
 184     * Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
 185     */
 186    getEvaluation(params: MachineLearning.Types.GetEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.GetEvaluationOutput) => void): Request<MachineLearning.Types.GetEvaluationOutput, AWSError>;
 187    /**
 188     * Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
 189     */
 190    getEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.GetEvaluationOutput) => void): Request<MachineLearning.Types.GetEvaluationOutput, AWSError>;
 191    /**
 192     * 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. 
 193     */
 194    getMLModel(params: MachineLearning.Types.GetMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.GetMLModelOutput) => void): Request<MachineLearning.Types.GetMLModelOutput, AWSError>;
 195    /**
 196     * 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. 
 197     */
 198    getMLModel(callback?: (err: AWSError, data: MachineLearning.Types.GetMLModelOutput) => void): Request<MachineLearning.Types.GetMLModelOutput, AWSError>;
 199    /**
 200     * 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.
 201     */
 202    predict(params: MachineLearning.Types.PredictInput, callback?: (err: AWSError, data: MachineLearning.Types.PredictOutput) => void): Request<MachineLearning.Types.PredictOutput, AWSError>;
 203    /**
 204     * 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.
 205     */
 206    predict(callback?: (err: AWSError, data: MachineLearning.Types.PredictOutput) => void): Request<MachineLearning.Types.PredictOutput, AWSError>;
 207    /**
 208     * Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element.
 209     */
 210    updateBatchPrediction(params: MachineLearning.Types.UpdateBatchPredictionInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateBatchPredictionOutput) => void): Request<MachineLearning.Types.UpdateBatchPredictionOutput, AWSError>;
 211    /**
 212     * Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element.
 213     */
 214    updateBatchPrediction(callback?: (err: AWSError, data: MachineLearning.Types.UpdateBatchPredictionOutput) => void): Request<MachineLearning.Types.UpdateBatchPredictionOutput, AWSError>;
 215    /**
 216     * Updates the DataSourceName of a DataSource. You can use the GetDataSource operation to view the contents of the updated data element.
 217     */
 218    updateDataSource(params: MachineLearning.Types.UpdateDataSourceInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateDataSourceOutput) => void): Request<MachineLearning.Types.UpdateDataSourceOutput, AWSError>;
 219    /**
 220     * Updates the DataSourceName of a DataSource. You can use the GetDataSource operation to view the contents of the updated data element.
 221     */
 222    updateDataSource(callback?: (err: AWSError, data: MachineLearning.Types.UpdateDataSourceOutput) => void): Request<MachineLearning.Types.UpdateDataSourceOutput, AWSError>;
 223    /**
 224     * Updates the EvaluationName of an Evaluation. You can use the GetEvaluation operation to view the contents of the updated data element.
 225     */
 226    updateEvaluation(params: MachineLearning.Types.UpdateEvaluationInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateEvaluationOutput) => void): Request<MachineLearning.Types.UpdateEvaluationOutput, AWSError>;
 227    /**
 228     * Updates the EvaluationName of an Evaluation. You can use the GetEvaluation operation to view the contents of the updated data element.
 229     */
 230    updateEvaluation(callback?: (err: AWSError, data: MachineLearning.Types.UpdateEvaluationOutput) => void): Request<MachineLearning.Types.UpdateEvaluationOutput, AWSError>;
 231    /**
 232     * Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element.
 233     */
 234    updateMLModel(params: MachineLearning.Types.UpdateMLModelInput, callback?: (err: AWSError, data: MachineLearning.Types.UpdateMLModelOutput) => void): Request<MachineLearning.Types.UpdateMLModelOutput, AWSError>;
 235    /**
 236     * Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element.
 237     */
 238    updateMLModel(callback?: (err: AWSError, data: MachineLearning.Types.UpdateMLModelOutput) => void): Request<MachineLearning.Types.UpdateMLModelOutput, AWSError>;
 239    /**
 240     * Waits for the dataSourceAvailable state by periodically calling the underlying MachineLearning.describeDataSourcesoperation every 30 seconds (at most 60 times).
 241     */
 242    waitFor(state: "dataSourceAvailable", params: MachineLearning.Types.DescribeDataSourcesInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
 243    /**
 244     * Waits for the dataSourceAvailable state by periodically calling the underlying MachineLearning.describeDataSourcesoperation every 30 seconds (at most 60 times).
 245     */
 246    waitFor(state: "dataSourceAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeDataSourcesOutput) => void): Request<MachineLearning.Types.DescribeDataSourcesOutput, AWSError>;
 247    /**
 248     * Waits for the mLModelAvailable state by periodically calling the underlying MachineLearning.describeMLModelsoperation every 30 seconds (at most 60 times).
 249     */
 250    waitFor(state: "mLModelAvailable", params: MachineLearning.Types.DescribeMLModelsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
 251    /**
 252     * Waits for the mLModelAvailable state by periodically calling the underlying MachineLearning.describeMLModelsoperation every 30 seconds (at most 60 times).
 253     */
 254    waitFor(state: "mLModelAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeMLModelsOutput) => void): Request<MachineLearning.Types.DescribeMLModelsOutput, AWSError>;
 255    /**
 256     * Waits for the evaluationAvailable state by periodically calling the underlying MachineLearning.describeEvaluationsoperation every 30 seconds (at most 60 times).
 257     */
 258    waitFor(state: "evaluationAvailable", params: MachineLearning.Types.DescribeEvaluationsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
 259    /**
 260     * Waits for the evaluationAvailable state by periodically calling the underlying MachineLearning.describeEvaluationsoperation every 30 seconds (at most 60 times).
 261     */
 262    waitFor(state: "evaluationAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeEvaluationsOutput) => void): Request<MachineLearning.Types.DescribeEvaluationsOutput, AWSError>;
 263    /**
 264     * Waits for the batchPredictionAvailable state by periodically calling the underlying MachineLearning.describeBatchPredictionsoperation every 30 seconds (at most 60 times).
 265     */
 266    waitFor(state: "batchPredictionAvailable", params: MachineLearning.Types.DescribeBatchPredictionsInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
 267    /**
 268     * Waits for the batchPredictionAvailable state by periodically calling the underlying MachineLearning.describeBatchPredictionsoperation every 30 seconds (at most 60 times).
 269     */
 270    waitFor(state: "batchPredictionAvailable", callback?: (err: AWSError, data: MachineLearning.Types.DescribeBatchPredictionsOutput) => void): Request<MachineLearning.Types.DescribeBatchPredictionsOutput, AWSError>;
 271  }
 272  declare namespace MachineLearning {
 273    export interface AddTagsInput {
 274      /**
 275       * 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.
 276       */
 277      Tags: TagList;
 278      /**
 279       * The ID of the ML object to tag. For example, exampleModelId.
 280       */
 281      ResourceId: EntityId;
 282      /**
 283       * The type of the ML object to tag.
 284       */
 285      ResourceType: TaggableResourceType;
 286    }
 287    export interface AddTagsOutput {
 288      /**
 289       * The ID of the ML object that was tagged.
 290       */
 291      ResourceId?: EntityId;
 292      /**
 293       * The type of the ML object that was tagged.
 294       */
 295      ResourceType?: TaggableResourceType;
 296    }
 297    export type Algorithm = "sgd"|string;
 298    export type AwsUserArn = string;
 299    export interface BatchPrediction {
 300      /**
 301       * The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. 
 302       */
 303      BatchPredictionId?: EntityId;
 304      /**
 305       * The ID of the MLModel that generated predictions for the BatchPrediction request.
 306       */
 307      MLModelId?: EntityId;
 308      /**
 309       * The ID of the DataSource that points to the group of observations to predict.
 310       */
 311      BatchPredictionDataSourceId?: EntityId;
 312      /**
 313       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
 314       */
 315      InputDataLocationS3?: S3Url;
 316      /**
 317       * 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.
 318       */
 319      CreatedByIamUser?: AwsUserArn;
 320      /**
 321       * The time that the BatchPrediction was created. The time is expressed in epoch time.
 322       */
 323      CreatedAt?: EpochTime;
 324      /**
 325       * The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.
 326       */
 327      LastUpdatedAt?: EpochTime;
 328      /**
 329       * A user-supplied name or description of the BatchPrediction.
 330       */
 331      Name?: EntityName;
 332      /**
 333       * 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.  
 334       */
 335      Status?: EntityStatus;
 336      /**
 337       * 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: ':', '//', '/./', '/../'.
 338       */
 339      OutputUri?: S3Url;
 340      /**
 341       * A description of the most recent details about processing the batch prediction request.
 342       */
 343      Message?: Message;
 344      ComputeTime?: LongType;
 345      FinishedAt?: EpochTime;
 346      StartedAt?: EpochTime;
 347      TotalRecordCount?: LongType;
 348      InvalidRecordCount?: LongType;
 349    }
 350    export type BatchPredictionFilterVariable = "CreatedAt"|"LastUpdatedAt"|"Status"|"Name"|"IAMUser"|"MLModelId"|"DataSourceId"|"DataURI"|string;
 351    export type BatchPredictions = BatchPrediction[];
 352    export type ComparatorValue = string;
 353    export type ComputeStatistics = boolean;
 354    export interface CreateBatchPredictionInput {
 355      /**
 356       * A user-supplied ID that uniquely identifies the BatchPrediction.
 357       */
 358      BatchPredictionId: EntityId;
 359      /**
 360       * A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.
 361       */
 362      BatchPredictionName?: EntityName;
 363      /**
 364       * The ID of the MLModel that will generate predictions for the group of observations. 
 365       */
 366      MLModelId: EntityId;
 367      /**
 368       * The ID of the DataSource that points to the group of observations to predict.
 369       */
 370      BatchPredictionDataSourceId: EntityId;
 371      /**
 372       * 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.
 373       */
 374      OutputUri: S3Url;
 375    }
 376    export interface CreateBatchPredictionOutput {
 377      /**
 378       * A user-supplied ID that uniquely identifies the BatchPrediction. This value is identical to the value of the BatchPredictionId in the request.
 379       */
 380      BatchPredictionId?: EntityId;
 381    }
 382    export interface CreateDataSourceFromRDSInput {
 383      /**
 384       * A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.
 385       */
 386      DataSourceId: EntityId;
 387      /**
 388       * A user-supplied name or description of the DataSource.
 389       */
 390      DataSourceName?: EntityName;
 391      /**
 392       * 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}}"   
 393       */
 394      RDSData: RDSDataSpec;
 395      /**
 396       * 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. 
 397       */
 398      RoleARN: RoleARN;
 399      /**
 400       * 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. 
 401       */
 402      ComputeStatistics?: ComputeStatistics;
 403    }
 404    export interface CreateDataSourceFromRDSOutput {
 405      /**
 406       * A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. 
 407       */
 408      DataSourceId?: EntityId;
 409    }
 410    export interface CreateDataSourceFromRedshiftInput {
 411      /**
 412       * A user-supplied ID that uniquely identifies the DataSource.
 413       */
 414      DataSourceId: EntityId;
 415      /**
 416       * A user-supplied name or description of the DataSource. 
 417       */
 418      DataSourceName?: EntityName;
 419      /**
 420       * 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}}"   
 421       */
 422      DataSpec: RedshiftDataSpec;
 423      /**
 424       * 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   
 425       */
 426      RoleARN: RoleARN;
 427      /**
 428       * 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.
 429       */
 430      ComputeStatistics?: ComputeStatistics;
 431    }
 432    export interface CreateDataSourceFromRedshiftOutput {
 433      /**
 434       * A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. 
 435       */
 436      DataSourceId?: EntityId;
 437    }
 438    export interface CreateDataSourceFromS3Input {
 439      /**
 440       * A user-supplied identifier that uniquely identifies the DataSource. 
 441       */
 442      DataSourceId: EntityId;
 443      /**
 444       * A user-supplied name or description of the DataSource. 
 445       */
 446      DataSourceName?: EntityName;
 447      /**
 448       * 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}}"   
 449       */
 450      DataSpec: S3DataSpec;
 451      /**
 452       * 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.
 453       */
 454      ComputeStatistics?: ComputeStatistics;
 455    }
 456    export interface CreateDataSourceFromS3Output {
 457      /**
 458       * A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request. 
 459       */
 460      DataSourceId?: EntityId;
 461    }
 462    export interface CreateEvaluationInput {
 463      /**
 464       * A user-supplied ID that uniquely identifies the Evaluation.
 465       */
 466      EvaluationId: EntityId;
 467      /**
 468       * A user-supplied name or description of the Evaluation.
 469       */
 470      EvaluationName?: EntityName;
 471      /**
 472       * 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.
 473       */
 474      MLModelId: EntityId;
 475      /**
 476       * The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.
 477       */
 478      EvaluationDataSourceId: EntityId;
 479    }
 480    export interface CreateEvaluationOutput {
 481      /**
 482       * The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.
 483       */
 484      EvaluationId?: EntityId;
 485    }
 486    export interface CreateMLModelInput {
 487      /**
 488       * A user-supplied ID that uniquely identifies the MLModel.
 489       */
 490      MLModelId: EntityId;
 491      /**
 492       * A user-supplied name or description of the MLModel.
 493       */
 494      MLModelName?: EntityName;
 495      /**
 496       * The category of supervised learning that this MLModel will address. Choose from the following types:   Choose REGRESSION if the MLModel will be used to predict a numeric value.   Choose BINARY if the MLModel result has two possible values.   Choose MULTICLASS if the MLModel result has a limited number of values.    For more information, see the Amazon Machine Learning Developer Guide.
 497       */
 498      MLModelType: MLModelType;
 499      /**
 500       * A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters:    sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.    sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.    sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.    sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.    sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.  
 501       */
 502      Parameters?: TrainingParameters;
 503      /**
 504       * The DataSource that points to the training data.
 505       */
 506      TrainingDataSourceId: EntityId;
 507      /**
 508       * The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
 509       */
 510      Recipe?: Recipe;
 511      /**
 512       * The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
 513       */
 514      RecipeUri?: S3Url;
 515    }
 516    export interface CreateMLModelOutput {
 517      /**
 518       * A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request. 
 519       */
 520      MLModelId?: EntityId;
 521    }
 522    export interface CreateRealtimeEndpointInput {
 523      /**
 524       * The ID assigned to the MLModel during creation.
 525       */
 526      MLModelId: EntityId;
 527    }
 528    export interface CreateRealtimeEndpointOutput {
 529      /**
 530       * A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.
 531       */
 532      MLModelId?: EntityId;
 533      /**
 534       * The endpoint information of the MLModel 
 535       */
 536      RealtimeEndpointInfo?: RealtimeEndpointInfo;
 537    }
 538    export type DataRearrangement = string;
 539    export type DataSchema = string;
 540    export interface DataSource {
 541      /**
 542       * The ID that is assigned to the DataSource during creation.
 543       */
 544      DataSourceId?: EntityId;
 545      /**
 546       * The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.
 547       */
 548      DataLocationS3?: S3Url;
 549      /**
 550       * A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.
 551       */
 552      DataRearrangement?: DataRearrangement;
 553      /**
 554       * The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
 555       */
 556      CreatedByIamUser?: AwsUserArn;
 557      /**
 558       * The time that the DataSource was created. The time is expressed in epoch time.
 559       */
 560      CreatedAt?: EpochTime;
 561      /**
 562       * The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.
 563       */
 564      LastUpdatedAt?: EpochTime;
 565      /**
 566       * The total number of observations contained in the data files that the DataSource references.
 567       */
 568      DataSizeInBytes?: LongType;
 569      /**
 570       * The number of data files referenced by the DataSource.
 571       */
 572      NumberOfFiles?: LongType;
 573      /**
 574       * A user-supplied name or description of the DataSource.
 575       */
 576      Name?: EntityName;
 577      /**
 578       * The current status of the DataSource. This element can have one of the following values:    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource.   INPROGRESS - The creation process is underway.   FAILED - The request to create a DataSource did not run to completion. It is not usable.   COMPLETED - The creation process completed successfully.   DELETED - The DataSource is marked as deleted. It is not usable.  
 579       */
 580      Status?: EntityStatus;
 581      /**
 582       * A description of the most recent details about creating the DataSource.
 583       */
 584      Message?: Message;
 585      RedshiftMetadata?: RedshiftMetadata;
 586      RDSMetadata?: RDSMetadata;
 587      RoleARN?: RoleARN;
 588      /**
 589       *  The parameter is true if statistics need to be generated from the observation data. 
 590       */
 591      ComputeStatistics?: ComputeStatistics;
 592      ComputeTime?: LongType;
 593      FinishedAt?: EpochTime;
 594      StartedAt?: EpochTime;
 595    }
 596    export type DataSourceFilterVariable = "CreatedAt"|"LastUpdatedAt"|"Status"|"Name"|"DataLocationS3"|"IAMUser"|string;
 597    export type DataSources = DataSource[];
 598    export interface DeleteBatchPredictionInput {
 599      /**
 600       * A user-supplied ID that uniquely identifies the BatchPrediction.
 601       */
 602      BatchPredictionId: EntityId;
 603    }
 604    export interface DeleteBatchPredictionOutput {
 605      /**
 606       * A user-supplied ID that uniquely identifies the BatchPrediction. This value should be identical to the value of the BatchPredictionID in the request.
 607       */
 608      BatchPredictionId?: EntityId;
 609    }
 610    export interface DeleteDataSourceInput {
 611      /**
 612       * A user-supplied ID that uniquely identifies the DataSource.
 613       */
 614      DataSourceId: EntityId;
 615    }
 616    export interface DeleteDataSourceOutput {
 617      /**
 618       * A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.
 619       */
 620      DataSourceId?: EntityId;
 621    }
 622    export interface DeleteEvaluationInput {
 623      /**
 624       * A user-supplied ID that uniquely identifies the Evaluation to delete.
 625       */
 626      EvaluationId: EntityId;
 627    }
 628    export interface DeleteEvaluationOutput {
 629      /**
 630       * A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.
 631       */
 632      EvaluationId?: EntityId;
 633    }
 634    export interface DeleteMLModelInput {
 635      /**
 636       * A user-supplied ID that uniquely identifies the MLModel.
 637       */
 638      MLModelId: EntityId;
 639    }
 640    export interface DeleteMLModelOutput {
 641      /**
 642       * A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelID in the request.
 643       */
 644      MLModelId?: EntityId;
 645    }
 646    export interface DeleteRealtimeEndpointInput {
 647      /**
 648       * The ID assigned to the MLModel during creation.
 649       */
 650      MLModelId: EntityId;
 651    }
 652    export interface DeleteRealtimeEndpointOutput {
 653      /**
 654       * A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.
 655       */
 656      MLModelId?: EntityId;
 657      /**
 658       * The endpoint information of the MLModel 
 659       */
 660      RealtimeEndpointInfo?: RealtimeEndpointInfo;
 661    }
 662    export interface DeleteTagsInput {
 663      /**
 664       * One or more tags to delete.
 665       */
 666      TagKeys: TagKeyList;
 667      /**
 668       * The ID of the tagged ML object. For example, exampleModelId.
 669       */
 670      ResourceId: EntityId;
 671      /**
 672       * The type of the tagged ML object.
 673       */
 674      ResourceType: TaggableResourceType;
 675    }
 676    export interface DeleteTagsOutput {
 677      /**
 678       * The ID of the ML object from which tags were deleted.
 679       */
 680      ResourceId?: EntityId;
 681      /**
 682       * The type of the ML object from which tags were deleted.
 683       */
 684      ResourceType?: TaggableResourceType;
 685    }
 686    export interface DescribeBatchPredictionsInput {
 687      /**
 688       * Use one of the following variables to filter a list of BatchPrediction:    CreatedAt - Sets the search criteria to the BatchPrediction creation date.    Status - Sets the search criteria to the BatchPrediction status.    Name - Sets the search criteria to the contents of the BatchPrediction   Name.    IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.    MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.    DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.    DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.  
 689       */
 690      FilterVariable?: BatchPredictionFilterVariable;
 691      /**
 692       * The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.
 693       */
 694      EQ?: ComparatorValue;
 695      /**
 696       * The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.
 697       */
 698      GT?: ComparatorValue;
 699      /**
 700       * The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.
 701       */
 702      LT?: ComparatorValue;
 703      /**
 704       * The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE. 
 705       */
 706      GE?: ComparatorValue;
 707      /**
 708       * The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.
 709       */
 710      LE?: ComparatorValue;
 711      /**
 712       * The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.
 713       */
 714      NE?: ComparatorValue;
 715      /**
 716       * A string that is found at the beginning of a variable, such as Name or Id. For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix:    2014-09   2014-09-09   2014-09-09-Holiday  
 717       */
 718      Prefix?: ComparatorValue;
 719      /**
 720       * A two-value parameter that determines the sequence of the resulting list of MLModels.    asc - Arranges the list in ascending order (A-Z, 0-9).    dsc - Arranges the list in descending order (Z-A, 9-0).   Results are sorted by FilterVariable.
 721       */
 722      SortOrder?: SortOrder;
 723      /**
 724       * An ID of the page in the paginated results.
 725       */
 726      NextToken?: StringType;
 727      /**
 728       * The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.
 729       */
 730      Limit?: PageLimit;
 731    }
 732    export interface DescribeBatchPredictionsOutput {
 733      /**
 734       * A list of BatchPrediction objects that meet the search criteria. 
 735       */
 736      Results?: BatchPredictions;
 737      /**
 738       * The ID of the next page in the paginated results that indicates at least one more page follows.
 739       */
 740      NextToken?: StringType;
 741    }
 742    export interface DescribeDataSourcesInput {
 743      /**
 744       * Use one of the following variables to filter a list of DataSource:    CreatedAt - Sets the search criteria to DataSource creation dates.    Status - Sets the search criteria to DataSource statuses.    Name - Sets the search criteria to the contents of DataSource Name.    DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.    IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.  
 745       */
 746      FilterVariable?: DataSourceFilterVariable;
 747      /**
 748       * The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ.
 749       */
 750      EQ?: ComparatorValue;
 751      /**
 752       * The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT.
 753       */
 754      GT?: ComparatorValue;
 755      /**
 756       * The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT.
 757       */
 758      LT?: ComparatorValue;
 759      /**
 760       * The greater than or equal to operator. The DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE. 
 761       */
 762      GE?: ComparatorValue;
 763      /**
 764       * The less than or equal to operator. The DataSource results will have FilterVariable values that are less than or equal to the value specified with LE.
 765       */
 766      LE?: ComparatorValue;
 767      /**
 768       * The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE.
 769       */
 770      NE?: ComparatorValue;
 771      /**
 772       * A string that is found at the beginning of a variable, such as Name or Id. For example, a DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this DataSource, select Name for the FilterVariable and any of the following strings for the Prefix:    2014-09   2014-09-09   2014-09-09-Holiday  
 773       */
 774      Prefix?: ComparatorValue;
 775      /**
 776       * A two-value parameter that determines the sequence of the resulting list of DataSource.    asc - Arranges the list in ascending order (A-Z, 0-9).    dsc - Arranges the list in descending order (Z-A, 9-0).   Results are sorted by FilterVariable.
 777       */
 778      SortOrder?: SortOrder;
 779      /**
 780       * The ID of the page in the paginated results.
 781       */
 782      NextToken?: StringType;
 783      /**
 784       *  The maximum number of DataSource to include in the result.
 785       */
 786      Limit?: PageLimit;
 787    }
 788    export interface DescribeDataSourcesOutput {
 789      /**
 790       * A list of DataSource that meet the search criteria. 
 791       */
 792      Results?: DataSources;
 793      /**
 794       * An ID of the next page in the paginated results that indicates at least one more page follows.
 795       */
 796      NextToken?: StringType;
 797    }
 798    export interface DescribeEvaluationsInput {
 799      /**
 800       * Use one of the following variable to filter a list of Evaluation objects:    CreatedAt - Sets the search criteria to the Evaluation creation date.    Status - Sets the search criteria to the Evaluation status.    Name - Sets the search criteria to the contents of Evaluation   Name.    IAMUser - Sets the search criteria to the user account that invoked an Evaluation.    MLModelId - Sets the search criteria to the MLModel that was evaluated.    DataSourceId - Sets the search criteria to the DataSource used in Evaluation.    DataUri - Sets the search criteria to the data file(s) used in Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.  
 801       */
 802      FilterVariable?: EvaluationFilterVariable;
 803      /**
 804       * The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ.
 805       */
 806      EQ?: ComparatorValue;
 807      /**
 808       * The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT.
 809       */
 810      GT?: ComparatorValue;
 811      /**
 812       * The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT.
 813       */
 814      LT?: ComparatorValue;
 815      /**
 816       * The greater than or equal to operator. The Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE. 
 817       */
 818      GE?: ComparatorValue;
 819      /**
 820       * The less than or equal to operator. The Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE.
 821       */
 822      LE?: ComparatorValue;
 823      /**
 824       * The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE.
 825       */
 826      NE?: ComparatorValue;
 827      /**
 828       * A string that is found at the beginning of a variable, such as Name or Id. For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this Evaluation, select Name for the FilterVariable and any of the following strings for the Prefix:    2014-09   2014-09-09   2014-09-09-Holiday  
 829       */
 830      Prefix?: ComparatorValue;
 831      /**
 832       * A two-value parameter that determines the sequence of the resulting list of Evaluation.    asc - Arranges the list in ascending order (A-Z, 0-9).    dsc - Arranges the list in descending order (Z-A, 9-0).   Results are sorted by FilterVariable.
 833       */
 834      SortOrder?: SortOrder;
 835      /**
 836       * The ID of the page in the paginated results.
 837       */
 838      NextToken?: StringType;
 839      /**
 840       *  The maximum number of Evaluation to include in the result.
 841       */
 842      Limit?: PageLimit;
 843    }
 844    export interface DescribeEvaluationsOutput {
 845      /**
 846       * A list of Evaluation that meet the search criteria. 
 847       */
 848      Results?: Evaluations;
 849      /**
 850       * The ID of the next page in the paginated results that indicates at least one more page follows.
 851       */
 852      NextToken?: StringType;
 853    }
 854    export interface DescribeMLModelsInput {
 855      /**
 856       * Use one of the following variables to filter a list of MLModel:    CreatedAt - Sets the search criteria to MLModel creation date.    Status - Sets the search criteria to MLModel status.    Name - Sets the search criteria to the contents of MLModel   Name.    IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.    TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel.    RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status.    MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class.    Algorithm - Sets the search criteria to the algorithm that the MLModel uses.    TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.  
 857       */
 858      FilterVariable?: MLModelFilterVariable;
 859      /**
 860       * The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.
 861       */
 862      EQ?: ComparatorValue;
 863      /**
 864       * The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.
 865       */
 866      GT?: ComparatorValue;
 867      /**
 868       * The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.
 869       */
 870      LT?: ComparatorValue;
 871      /**
 872       * The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE. 
 873       */
 874      GE?: ComparatorValue;
 875      /**
 876       * The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE.
 877       */
 878      LE?: ComparatorValue;
 879      /**
 880       * The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.
 881       */
 882      NE?: ComparatorValue;
 883      /**
 884       * A string that is found at the beginning of a variable, such as Name or Id. For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix:    2014-09   2014-09-09   2014-09-09-Holiday  
 885       */
 886      Prefix?: ComparatorValue;
 887      /**
 888       * A two-value parameter that determines the sequence of the resulting list of MLModel.    asc - Arranges the list in ascending order (A-Z, 0-9).    dsc - Arranges the list in descending order (Z-A, 9-0).   Results are sorted by FilterVariable.
 889       */
 890      SortOrder?: SortOrder;
 891      /**
 892       * The ID of the page in the paginated results.
 893       */
 894      NextToken?: StringType;
 895      /**
 896       * The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.
 897       */
 898      Limit?: PageLimit;
 899    }
 900    export interface DescribeMLModelsOutput {
 901      /**
 902       * A list of MLModel that meet the search criteria.
 903       */
 904      Results?: MLModels;
 905      /**
 906       * The ID of the next page in the paginated results that indicates at least one more page follows.
 907       */
 908      NextToken?: StringType;
 909    }
 910    export interface DescribeTagsInput {
 911      /**
 912       * The ID of the ML object. For example, exampleModelId. 
 913       */
 914      ResourceId: EntityId;
 915      /**
 916       * The type of the ML object.
 917       */
 918      ResourceType: TaggableResourceType;
 919    }
 920    export interface DescribeTagsOutput {
 921      /**
 922       * The ID of the tagged ML object.
 923       */
 924      ResourceId?: EntityId;
 925      /**
 926       * The type of the tagged ML object.
 927       */
 928      ResourceType?: TaggableResourceType;
 929      /**
 930       * A list of tags associated with the ML object.
 931       */
 932      Tags?: TagList;
 933    }
 934    export type DetailsAttributes = "PredictiveModelType"|"Algorithm"|string;
 935    export type DetailsMap = {[key: string]: DetailsValue};
 936    export type DetailsValue = string;
 937    export type EDPPipelineId = string;
 938    export type EDPResourceRole = string;
 939    export type EDPSecurityGroupId = string;
 940    export type EDPSecurityGroupIds = EDPSecurityGroupId[];
 941    export type EDPServiceRole = string;
 942    export type EDPSubnetId = string;
 943    export type EntityId = string;
 944    export type EntityName = string;
 945    export type EntityStatus = "PENDING"|"INPROGRESS"|"FAILED"|"COMPLETED"|"DELETED"|string;
 946    export type EpochTime = Date;
 947    export interface Evaluation {
 948      /**
 949       * The ID that is assigned to the Evaluation at creation.
 950       */
 951      EvaluationId?: EntityId;
 952      /**
 953       * The ID of the MLModel that is the focus of the evaluation.
 954       */
 955      MLModelId?: EntityId;
 956      /**
 957       * The ID of the DataSource that is used to evaluate the MLModel.
 958       */
 959      EvaluationDataSourceId?: EntityId;
 960      /**
 961       * The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
 962       */
 963      InputDataLocationS3?: S3Url;
 964      /**
 965       * The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
 966       */
 967      CreatedByIamUser?: AwsUserArn;
 968      /**
 969       * The time that the Evaluation was created. The time is expressed in epoch time.
 970       */
 971      CreatedAt?: EpochTime;
 972      /**
 973       * The time of the most recent edit to the Evaluation. The time is expressed in epoch time.
 974       */
 975      LastUpdatedAt?: EpochTime;
 976      /**
 977       * A user-supplied name or description of the Evaluation. 
 978       */
 979      Name?: EntityName;
 980      /**
 981       * The status of the evaluation. This element can have one of the following values:    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel.    INPROGRESS - The evaluation is underway.    FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.    COMPLETED - The evaluation process completed successfully.    DELETED - The Evaluation is marked as deleted. It is not usable.  
 982       */
 983      Status?: EntityStatus;
 984      /**
 985       * Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel:    BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.    RegressionRMSE: A 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: A multiclass MLModel uses the F1 score technique to measure performance.     For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. 
 986       */
 987      PerformanceMetrics?: PerformanceMetrics;
 988      /**
 989       * A description of the most recent details about evaluating the MLModel.
 990       */
 991      Message?: Message;
 992      ComputeTime?: LongType;
 993      FinishedAt?: EpochTime;
 994      StartedAt?: EpochTime;
 995    }
 996    export type EvaluationFilterVariable = "CreatedAt"|"LastUpdatedAt"|"Status"|"Name"|"IAMUser"|"MLModelId"|"DataSourceId"|"DataURI"|string;
 997    export type Evaluations = Evaluation[];
 998    export interface GetBatchPredictionInput {
 999      /**
1000       * An ID assigned to the BatchPrediction at creation.
1001       */
1002      BatchPredictionId: EntityId;
1003    }
1004    export interface GetBatchPredictionOutput {
1005      /**
1006       * An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.
1007       */
1008      BatchPredictionId?: EntityId;
1009      /**
1010       * The ID of the MLModel that generated predictions for the BatchPrediction request.
1011       */
1012      MLModelId?: EntityId;
1013      /**
1014       * The ID of the DataSource that was used to create the BatchPrediction. 
1015       */
1016      BatchPredictionDataSourceId?: EntityId;
1017      /**
1018       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
1019       */
1020      InputDataLocationS3?: S3Url;
1021      /**
1022       * 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.
1023       */
1024      CreatedByIamUser?: AwsUserArn;
1025      /**
1026       * The time when the BatchPrediction was created. The time is expressed in epoch time.
1027       */
1028      CreatedAt?: EpochTime;
1029      /**
1030       * The time of the most recent edit to BatchPrediction. The time is expressed in epoch time.
1031       */
1032      LastUpdatedAt?: EpochTime;
1033      /**
1034       * A user-supplied name or description of the BatchPrediction.
1035       */
1036      Name?: EntityName;
1037      /**
1038       * The status of the BatchPrediction, which can be one of the following values:    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.    INPROGRESS - The batch predictions are in progress.    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.  
1039       */
1040      Status?: EntityStatus;
1041      /**
1042       * The location of an Amazon S3 bucket or directory to receive the operation results.
1043       */
1044      OutputUri?: S3Url;
1045      /**
1046       * A link to the file that contains logs of the CreateBatchPrediction operation.
1047       */
1048      LogUri?: PresignedS3Url;
1049      /**
1050       * A description of the most recent details about processing the batch prediction request.
1051       */
1052      Message?: Message;
1053      /**
1054       * The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state.
1055       */
1056      ComputeTime?: LongType;
1057      /**
1058       * The epoch time when Amazon Machine Learning marked the BatchPrediction as COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction is in the COMPLETED or FAILED state.
1059       */
1060      FinishedAt?: EpochTime;
1061      /**
1062       * The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state.
1063       */
1064      StartedAt?: EpochTime;
1065      /**
1066       * The number of total records that Amazon Machine Learning saw while processing the BatchPrediction.
1067       */
1068      TotalRecordCount?: LongType;
1069      /**
1070       * The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction.
1071       */
1072      InvalidRecordCount?: LongType;
1073    }
1074    export interface GetDataSourceInput {
1075      /**
1076       * The ID assigned to the DataSource at creation.
1077       */
1078      DataSourceId: EntityId;
1079      /**
1080       * Specifies whether the GetDataSource operation should return DataSourceSchema. If true, DataSourceSchema is returned. If false, DataSourceSchema is not returned.
1081       */
1082      Verbose?: Verbose;
1083    }
1084    export interface GetDataSourceOutput {
1085      /**
1086       * The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.
1087       */
1088      DataSourceId?: EntityId;
1089      /**
1090       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
1091       */
1092      DataLocationS3?: S3Url;
1093      /**
1094       * A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.
1095       */
1096      DataRearrangement?: DataRearrangement;
1097      /**
1098       * The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
1099       */
1100      CreatedByIamUser?: AwsUserArn;
1101      /**
1102       * The time that the DataSource was created. The time is expressed in epoch time.
1103       */
1104      CreatedAt?: EpochTime;
1105      /**
1106       * The time of the most recent edit to the DataSource. The time is expressed in epoch time.
1107       */
1108      LastUpdatedAt?: EpochTime;
1109      /**
1110       * The total size of observations in the data files.
1111       */
1112      DataSizeInBytes?: LongType;
1113      /**
1114       * The number of data files referenced by the DataSource.
1115       */
1116      NumberOfFiles?: LongType;
1117      /**
1118       * A user-supplied name or description of the DataSource.
1119       */
1120      Name?: EntityName;
1121      /**
1122       * The current status of the DataSource. This element can have one of the following values:    PENDING - Amazon ML submitted a request to create a DataSource.    INPROGRESS - The creation process is underway.    FAILED - The request to create a DataSource did not run to completion. It is not usable.    COMPLETED - The creation process completed successfully.    DELETED - The DataSource is marked as deleted. It is not usable.  
1123       */
1124      Status?: EntityStatus;
1125      /**
1126       * A link to the file containing logs of CreateDataSourceFrom* operations.
1127       */
1128      LogUri?: PresignedS3Url;
1129      /**
1130       * The user-supplied description of the most recent details about creating the DataSource.
1131       */
1132      Message?: Message;
1133      RedshiftMetadata?: RedshiftMetadata;
1134      RDSMetadata?: RDSMetadata;
1135      RoleARN?: RoleARN;
1136      /**
1137       *  The parameter is true if statistics need to be generated from the observation data. 
1138       */
1139      ComputeStatistics?: ComputeStatistics;
1140      /**
1141       * The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the DataSource, normalized and scaled on computation resources. ComputeTime is only available if the DataSource is in the COMPLETED state and the ComputeStatistics is set to true.
1142       */
1143      ComputeTime?: LongType;
1144      /**
1145       * The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED or FAILED state.
1146       */
1147      FinishedAt?: EpochTime;
1148      /**
1149       * The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state.
1150       */
1151      StartedAt?: EpochTime;
1152      /**
1153       * The schema used by all of the data files of this DataSource.  Note: This parameter is provided as part of the verbose format.
1154       */
1155      DataSourceSchema?: DataSchema;
1156    }
1157    export interface GetEvaluationInput {
1158      /**
1159       * The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information. 
1160       */
1161      EvaluationId: EntityId;
1162    }
1163    export interface GetEvaluationOutput {
1164      /**
1165       * The evaluation ID which is same as the EvaluationId in the request.
1166       */
1167      EvaluationId?: EntityId;
1168      /**
1169       * The ID of the MLModel that was the focus of the evaluation.
1170       */
1171      MLModelId?: EntityId;
1172      /**
1173       * The DataSource used for this evaluation.
1174       */
1175      EvaluationDataSourceId?: EntityId;
1176      /**
1177       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
1178       */
1179      InputDataLocationS3?: S3Url;
1180      /**
1181       * The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
1182       */
1183      CreatedByIamUser?: AwsUserArn;
1184      /**
1185       * The time that the Evaluation was created. The time is expressed in epoch time.
1186       */
1187      CreatedAt?: EpochTime;
1188      /**
1189       * The time of the most recent edit to the Evaluation. The time is expressed in epoch time.
1190       */
1191      LastUpdatedAt?: EpochTime;
1192      /**
1193       * A user-supplied name or description of the Evaluation. 
1194       */
1195      Name?: EntityName;
1196      /**
1197       * The status of the evaluation. This element can have one of the following values:    PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel.    INPROGRESS - The evaluation is underway.    FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.    COMPLETED - The evaluation process completed successfully.    DELETED - The Evaluation is marked as deleted. It is not usable.  
1198       */
1199      Status?: EntityStatus;
1200      /**
1201       * Measurements of how well the MLModel performed using observations referenced by the DataSource. One of the following metric is returned based on the type of the MLModel:    BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.    RegressionRMSE: A 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: A multiclass MLModel uses the F1 score technique to measure performance.     For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. 
1202       */
1203      PerformanceMetrics?: PerformanceMetrics;
1204      /**
1205       * A link to the file that contains logs of the CreateEvaluation operation.
1206       */
1207      LogUri?: PresignedS3Url;
1208      /**
1209       * A description of the most recent details about evaluating the MLModel.
1210       */
1211      Message?: Message;
1212      /**
1213       * The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.
1214       */
1215      ComputeTime?: LongType;
1216      /**
1217       * The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.
1218       */
1219      FinishedAt?: EpochTime;
1220      /**
1221       * The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.
1222       */
1223      StartedAt?: EpochTime;
1224    }
1225    export interface GetMLModelInput {
1226      /**
1227       * The ID assigned to the MLModel at creation.
1228       */
1229      MLModelId: EntityId;
1230      /**
1231       * Specifies whether the GetMLModel operation should return Recipe. If true, Recipe is returned. If false, Recipe is not returned.
1232       */
1233      Verbose?: Verbose;
1234    }
1235    export interface GetMLModelOutput {
1236      /**
1237       * The MLModel ID, which is same as the MLModelId in the request.
1238       */
1239      MLModelId?: EntityId;
1240      /**
1241       * The ID of the training DataSource.
1242       */
1243      TrainingDataSourceId?: EntityId;
1244      /**
1245       * The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
1246       */
1247      CreatedByIamUser?: AwsUserArn;
1248      /**
1249       * The time that the MLModel was created. The time is expressed in epoch time.
1250       */
1251      CreatedAt?: EpochTime;
1252      /**
1253       * The time of the most recent edit to the MLModel. The time is expressed in epoch time.
1254       */
1255      LastUpdatedAt?: EpochTime;
1256      /**
1257       * A user-supplied name or description of the MLModel.
1258       */
1259      Name?: MLModelName;
1260      /**
1261       * The current status of the MLModel. This element can have one of the following values:    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.    INPROGRESS - The request is processing.    FAILED - The request did not run to completion. The ML model isn't usable.    COMPLETED - The request completed successfully.    DELETED - The MLModel is marked as deleted. It isn't usable.  
1262       */
1263      Status?: EntityStatus;
1264      SizeInBytes?: LongType;
1265      /**
1266       * The current endpoint of the MLModel 
1267       */
1268      EndpointInfo?: RealtimeEndpointInfo;
1269      /**
1270       * A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters:    sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.    sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.    sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.    sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.    sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.  
1271       */
1272      TrainingParameters?: TrainingParameters;
1273      /**
1274       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
1275       */
1276      InputDataLocationS3?: S3Url;
1277      /**
1278       * Identifies the MLModel category. The following are the available types:    REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"   BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"   MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"  
1279       */
1280      MLModelType?: MLModelType;
1281      /**
1282       * The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.
1283       */
1284      ScoreThreshold?: ScoreThreshold;
1285      /**
1286       * The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
1287       */
1288      ScoreThresholdLastUpdatedAt?: EpochTime;
1289      /**
1290       * A link to the file that contains logs of the CreateMLModel operation.
1291       */
1292      LogUri?: PresignedS3Url;
1293      /**
1294       * A description of the most recent details about accessing the MLModel.
1295       */
1296      Message?: Message;
1297      /**
1298       * The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.
1299       */
1300      ComputeTime?: LongType;
1301      /**
1302       * The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.
1303       */
1304      FinishedAt?: EpochTime;
1305      /**
1306       * The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.
1307       */
1308      StartedAt?: EpochTime;
1309      /**
1310       * The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.  Note: This parameter is provided as part of the verbose format.
1311       */
1312      Recipe?: Recipe;
1313      /**
1314       * The schema used by all of the data files referenced by the DataSource.  Note: This parameter is provided as part of the verbose format.
1315       */
1316      Schema?: DataSchema;
1317    }
1318    export type IntegerType = number;
1319    export type Label = string;
1320    export type LongType = number;
1321    export interface MLModel {
1322      /**
1323       * The ID assigned to the MLModel at creation.
1324       */
1325      MLModelId?: EntityId;
1326      /**
1327       * The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
1328       */
1329      TrainingDataSourceId?: EntityId;
1330      /**
1331       * The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
1332       */
1333      CreatedByIamUser?: AwsUserArn;
1334      /**
1335       * The time that the MLModel was created. The time is expressed in epoch time.
1336       */
1337      CreatedAt?: EpochTime;
1338      /**
1339       * The time of the most recent edit to the MLModel. The time is expressed in epoch time.
1340       */
1341      LastUpdatedAt?: EpochTime;
1342      /**
1343       * A user-supplied name or description of the MLModel.
1344       */
1345      Name?: MLModelName;
1346      /**
1347       * The current status of an MLModel. This element can have one of the following values:     PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.    INPROGRESS - The creation process is underway.    FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.    COMPLETED - The creation process completed successfully.    DELETED - The MLModel is marked as deleted. It isn't usable.  
1348       */
1349      Status?: EntityStatus;
1350      SizeInBytes?: LongType;
1351      /**
1352       * The current endpoint of the MLModel.
1353       */
1354      EndpointInfo?: RealtimeEndpointInfo;
1355      /**
1356       * A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters:    sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.    sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.    sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.    sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.    sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.  
1357       */
1358      TrainingParameters?: TrainingParameters;
1359      /**
1360       * The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
1361       */
1362      InputDataLocationS3?: S3Url;
1363      /**
1364       * The algorithm used to train the MLModel. The following algorithm is supported:    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.   
1365       */
1366      Algorithm?: Algorithm;
1367      /**
1368       * Identifies the MLModel category. The following are the available types:    REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".    MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".  
1369       */
1370      MLModelType?: MLModelType;
1371      ScoreThreshold?: ScoreThreshold;
1372      /**
1373       * The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
1374       */
1375      ScoreThresholdLastUpdatedAt?: EpochTime;
1376      /**
1377       * A description of the most recent details about accessing the MLModel.
1378       */
1379      Message?: Message;
1380      ComputeTime?: LongType;
1381      FinishedAt?: EpochTime;
1382      StartedAt?: EpochTime;
1383    }
1384    export type MLModelFilterVariable = "CreatedAt"|"LastUpdatedAt"|"Status"|"Name"|"IAMUser"|"TrainingDataSourceId"|"RealtimeEndpointStatus"|"MLModelType"|"Algorithm"|"TrainingDataURI"|string;
1385    export type MLModelName = string;
1386    export type MLModelType = "REGRESSION"|"BINARY"|"MULTICLASS"|string;
1387    export type MLModels = MLModel[];
1388    export type Message = string;
1389    export type PageLimit = number;
1390    export interface PerformanceMetrics {
1391      Properties?: PerformanceMetricsProperties;
1392    }
1393    export type PerformanceMetricsProperties = {[key: string]: PerformanceMetricsPropertyValue};
1394    export type PerformanceMetricsPropertyKey = string;
1395    export type PerformanceMetricsPropertyValue = string;
1396    export interface PredictInput {
1397      /**
1398       * A unique identifier of the MLModel.
1399       */
1400      MLModelId: EntityId;
1401      Record: Record;
1402      PredictEndpoint: VipURL;
1403    }
1404    export interface PredictOutput {
1405      Prediction?: Prediction;
1406    }
1407    export interface Prediction {
1408      /**
1409       * The prediction label for either a BINARY or MULTICLASS MLModel.
1410       */
1411      predictedLabel?: Label;
1412      /**
1413       * The prediction value for REGRESSION MLModel.
1414       */
1415      predictedValue?: floatLabel;
1416      predictedScores?: ScoreValuePerLabelMap;
1417      details?: DetailsMap;
1418    }
1419    export type PresignedS3Url = string;
1420    export interface RDSDataSpec {
1421      /**
1422       * Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
1423       */
1424      DatabaseInformation: RDSDatabase;
1425      /**
1426       * The query that is used to retrieve the observation data for the DataSource.
1427       */
1428      SelectSqlQuery: RDSSelectSqlQuery;
1429      /**
1430       * The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
1431       */
1432      DatabaseCredentials: RDSDatabaseCredentials;
1433      /**
1434       * The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
1435       */
1436      S3StagingLocation: S3Url;
1437      /**
1438       * A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource:     percentBegin   Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     percentEnd   Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     complement   The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}  Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}      strategy   To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}   
1439       */
1440      DataRearrangement?: DataRearrangement;
1441      /**
1442       * A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. A DataSchema is not required if you specify a DataSchemaUri  Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] }
1443       */
1444      DataSchema?: DataSchema;
1445      /**
1446       * The Amazon S3 location of the DataSchema. 
1447       */
1448      DataSchemaUri?: S3Url;
1449      /**
1450       * The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
1451       */
1452      ResourceRole: EDPResourceRole;
1453      /**
1454       * The role (DataPipelineDefaultRole) assumed by 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.
1455       */
1456      ServiceRole: EDPServiceRole;
1457      /**
1458       * The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
1459       */
1460      SubnetId: EDPSubnetId;
1461      /**
1462       * The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
1463       */
1464      SecurityGroupIds: EDPSecurityGroupIds;
1465    }
1466    export interface RDSDatabase {
1467      /**
1468       * The ID of an RDS DB instance.
1469       */
1470      InstanceIdentifier: RDSInstanceIdentifier;
1471      DatabaseName: RDSDatabaseName;
1472    }
1473    export interface RDSDatabaseCredentials {
1474      Username: RDSDatabaseUsername;
1475      Password: RDSDatabasePassword;
1476    }
1477    export type RDSDatabaseName = string;
1478    export type RDSDatabasePassword = string;
1479    export type RDSDatabaseUsername = string;
1480    export type RDSInstanceIdentifier = string;
1481    export interface RDSMetadata {
1482      /**
1483       * The database details required to connect to an Amazon RDS.
1484       */
1485      Database?: RDSDatabase;
1486      DatabaseUserName?: RDSDatabaseUsername;
1487      /**
1488       * The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput. 
1489       */
1490      SelectSqlQuery?: RDSSelectSqlQuery;
1491      /**
1492       * The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
1493       */
1494      ResourceRole?: EDPResourceRole;
1495      /**
1496       * The role (DataPipelineDefaultRole) assumed by the 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.
1497       */
1498      ServiceRole?: EDPServiceRole;
1499      /**
1500       * The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.
1501       */
1502      DataPipelineId?: EDPPipelineId;
1503    }
1504    export type RDSSelectSqlQuery = string;
1505    export interface RealtimeEndpointInfo {
1506      /**
1507       *  The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.
1508       */
1509      PeakRequestsPerSecond?: IntegerType;
1510      /**
1511       * The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.
1512       */
1513      CreatedAt?: EpochTime;
1514      /**
1515       * The URI that specifies where to send real-time prediction requests for the MLModel.  Note: The application must wait until the real-time endpoint is ready before using this URI.
1516       */
1517      EndpointUrl?: VipURL;
1518      /**
1519       *  The current status of the real-time endpoint for the MLModel. This element can have one of the following values:     NONE - Endpoint does not exist or was previously deleted.    READY - Endpoint is ready to be used for real-time predictions.    UPDATING - Updating/creating the endpoint.   
1520       */
1521      EndpointStatus?: RealtimeEndpointStatus;
1522    }
1523    export type RealtimeEndpointStatus = "NONE"|"READY"|"UPDATING"|"FAILED"|string;
1524    export type Recipe = string;
1525    export type Record = {[key: string]: VariableValue};
1526    export type RedshiftClusterIdentifier = string;
1527    export interface RedshiftDataSpec {
1528      /**
1529       * Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
1530       */
1531      DatabaseInformation: RedshiftDatabase;
1532      /**
1533       * Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.
1534       */
1535      SelectSqlQuery: RedshiftSelectSqlQuery;
1536      /**
1537       * Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
1538       */
1539      DatabaseCredentials: RedshiftDatabaseCredentials;
1540      /**
1541       * Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
1542       */
1543      S3StagingLocation: S3Url;
1544      /**
1545       * A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource:     percentBegin   Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     percentEnd   Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     complement   The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}  Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}      strategy   To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}   
1546       */
1547      DataRearrangement?: DataRearrangement;
1548      /**
1549       * A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. A DataSchema is not required if you specify a DataSchemaUri. Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] }
1550       */
1551      DataSchema?: DataSchema;
1552      /**
1553       * Describes the schema location for an Amazon Redshift DataSource.
1554       */
1555      DataSchemaUri?: S3Url;
1556    }
1557    export interface RedshiftDatabase {
1558      DatabaseName: RedshiftDatabaseName;
1559      ClusterIdentifier: RedshiftClusterIdentifier;
1560    }
1561    export interface RedshiftDatabaseCredentials {
1562      Username: RedshiftDatabaseUsername;
1563      Password: RedshiftDatabasePassword;
1564    }
1565    export type RedshiftDatabaseName = string;
1566    export type RedshiftDatabasePassword = string;
1567    export type RedshiftDatabaseUsername = string;
1568    export interface RedshiftMetadata {
1569      RedshiftDatabase?: RedshiftDatabase;
1570      DatabaseUserName?: RedshiftDatabaseUsername;
1571      /**
1572       *  The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput. 
1573       */
1574      SelectSqlQuery?: RedshiftSelectSqlQuery;
1575    }
1576    export type RedshiftSelectSqlQuery = string;
1577    export type RoleARN = string;
1578    export interface S3DataSpec {
1579      /**
1580       * The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.
1581       */
1582      DataLocationS3: S3Url;
1583      /**
1584       * A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource:     percentBegin   Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     percentEnd   Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.     complement   The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}  Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}      strategy   To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}   
1585       */
1586      DataRearrangement?: DataRearrangement;
1587      /**
1588       *  A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. You must provide either the DataSchema or the DataSchemaLocationS3. Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] }
1589       */
1590      DataSchema?: DataSchema;
1591      /**
1592       * Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3.
1593       */
1594      DataSchemaLocationS3?: S3Url;
1595    }
1596    export type S3Url = string;
1597    export type ScoreThreshold = number;
1598    export type ScoreValue = number;
1599    export type ScoreValuePerLabelMap = {[key: string]: ScoreValue};
1600    export type SortOrder = "asc"|"dsc"|string;
1601    export type StringType = string;
1602    export interface Tag {
1603      /**
1604       * A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
1605       */
1606      Key?: TagKey;
1607      /**
1608       * An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
1609       */
1610      Value?: TagValue;
1611    }
1612    export type TagKey = string;
1613    export type TagKeyList = TagKey[];
1614    export type TagList = Tag[];
1615    export type TagValue = string;
1616    export type TaggableResourceType = "BatchPrediction"|"DataSource"|"Evaluation"|"MLModel"|string;
1617    export type TrainingParameters = {[key: string]: StringType};
1618    export interface UpdateBatchPredictionInput {
1619      /**
1620       * The ID assigned to the BatchPrediction during creation.
1621       */
1622      BatchPredictionId: EntityId;
1623      /**
1624       * A new user-supplied name or description of the BatchPrediction.
1625       */
1626      BatchPredictionName: EntityName;
1627    }
1628    export interface UpdateBatchPredictionOutput {
1629      /**
1630       * The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.
1631       */
1632      BatchPredictionId?: EntityId;
1633    }
1634    export interface UpdateDataSourceInput {
1635      /**
1636       * The ID assigned to the DataSource during creation.
1637       */
1638      DataSourceId: EntityId;
1639      /**
1640       * A new user-supplied name or description of the DataSource that will replace the current description. 
1641       */
1642      DataSourceName: EntityName;
1643    }
1644    export interface UpdateDataSourceOutput {
1645      /**
1646       * The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request.
1647       */
1648      DataSourceId?: EntityId;
1649    }
1650    export interface UpdateEvaluationInput {
1651      /**
1652       * The ID assigned to the Evaluation during creation.
1653       */
1654      EvaluationId: EntityId;
1655      /**
1656       * A new user-supplied name or description of the Evaluation that will replace the current content. 
1657       */
1658      EvaluationName: EntityName;
1659    }
1660    export interface UpdateEvaluationOutput {
1661      /**
1662       * The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request.
1663       */
1664      EvaluationId?: EntityId;
1665    }
1666    export interface UpdateMLModelInput {
1667      /**
1668       * The ID assigned to the MLModel during creation.
1669       */
1670      MLModelId: EntityId;
1671      /**
1672       * A user-supplied name or description of the MLModel.
1673       */
1674      MLModelName?: EntityName;
1675      /**
1676       * The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false.
1677       */
1678      ScoreThreshold?: ScoreThreshold;
1679    }
1680    export interface UpdateMLModelOutput {
1681      /**
1682       * The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.
1683       */
1684      MLModelId?: EntityId;
1685    }
1686    export type VariableName = string;
1687    export type VariableValue = string;
1688    export type Verbose = boolean;
1689    export type VipURL = string;
1690    export type floatLabel = number;
1691    /**
1692     * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
1693     */
1694    export type apiVersion = "2014-12-12"|"latest"|string;
1695    export interface ClientApiVersions {
1696      /**
1697       * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
1698       */
1699      apiVersion?: apiVersion;
1700    }
1701    export type ClientConfiguration = ServiceConfigurationOptions & ClientApiVersions;
1702    /**
1703     * Contains interfaces for use with the MachineLearning client.
1704     */
1705    export import Types = MachineLearning;
1706  }
1707  export = MachineLearning;