Documentation

Index

Constants

This section is empty.

Variables

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Functions

This section is empty.

Types

type Algorithm

type Algorithm string
const (
	AlgorithmSgd Algorithm = "sgd"
)

    Enum values for Algorithm

    func (Algorithm) Values

    func (Algorithm) Values() []Algorithm

      Values returns all known values for Algorithm. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

      type BatchPrediction

      type BatchPrediction struct {
      
      	// The ID of the DataSource that points to the group of observations to predict.
      	BatchPredictionDataSourceId *string
      
      	// The ID assigned to the BatchPrediction at creation. This value should be
      	// identical to the value of the BatchPredictionID in the request.
      	BatchPredictionId *string
      
      	// Long integer type that is a 64-bit signed number.
      	ComputeTime *int64
      
      	// The time that the BatchPrediction was created. The time is expressed in epoch
      	// time.
      	CreatedAt *time.Time
      
      	// The AWS user account that invoked the BatchPrediction. The account type can be
      	// either an AWS root account or an AWS Identity and Access Management (IAM) user
      	// account.
      	CreatedByIamUser *string
      
      	// A timestamp represented in epoch time.
      	FinishedAt *time.Time
      
      	// The location of the data file or directory in Amazon Simple Storage Service
      	// (Amazon S3).
      	InputDataLocationS3 *string
      
      	// Long integer type that is a 64-bit signed number.
      	InvalidRecordCount *int64
      
      	// The time of the most recent edit to the BatchPrediction. The time is expressed
      	// in epoch time.
      	LastUpdatedAt *time.Time
      
      	// The ID of the MLModel that generated predictions for the BatchPrediction
      	// request.
      	MLModelId *string
      
      	// A description of the most recent details about processing the batch prediction
      	// request.
      	Message *string
      
      	// A user-supplied name or description of the BatchPrediction.
      	Name *string
      
      	// The location of an Amazon S3 bucket or directory to receive the operation
      	// results. The following substrings are not allowed in the s3 key portion of the
      	// outputURI field: ':', '//', '/./', '/../'.
      	OutputUri *string
      
      	// A timestamp represented in epoch time.
      	StartedAt *time.Time
      
      	// The status of the BatchPrediction. This element can have one of the following
      	// values:
      	//
      	// * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
      	// generate predictions for a batch of observations.
      	//
      	// * INPROGRESS - The process is
      	// underway.
      	//
      	// * FAILED - The request to perform a batch prediction did not run to
      	// completion. It is not usable.
      	//
      	// * COMPLETED - The batch prediction process
      	// completed successfully.
      	//
      	// * DELETED - The BatchPrediction is marked as deleted.
      	// It is not usable.
      	Status EntityStatus
      
      	// Long integer type that is a 64-bit signed number.
      	TotalRecordCount *int64
      }

        Represents the output of a GetBatchPrediction operation. The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

        type BatchPredictionFilterVariable

        type BatchPredictionFilterVariable string
        const (
        	BatchPredictionFilterVariableCreatedAt     BatchPredictionFilterVariable = "CreatedAt"
        	BatchPredictionFilterVariableLastUpdatedAt BatchPredictionFilterVariable = "LastUpdatedAt"
        	BatchPredictionFilterVariableStatus        BatchPredictionFilterVariable = "Status"
        	BatchPredictionFilterVariableName          BatchPredictionFilterVariable = "Name"
        	BatchPredictionFilterVariableIamUser       BatchPredictionFilterVariable = "IAMUser"
        	BatchPredictionFilterVariableMlModelId     BatchPredictionFilterVariable = "MLModelId"
        	BatchPredictionFilterVariableDatasourceId  BatchPredictionFilterVariable = "DataSourceId"
        	BatchPredictionFilterVariableDataUri       BatchPredictionFilterVariable = "DataURI"
        )

          Enum values for BatchPredictionFilterVariable

          func (BatchPredictionFilterVariable) Values

            Values returns all known values for BatchPredictionFilterVariable. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

            type DataSource

            type DataSource struct {
            
            	// The parameter is true if statistics need to be generated from the observation
            	// data.
            	ComputeStatistics bool
            
            	// Long integer type that is a 64-bit signed number.
            	ComputeTime *int64
            
            	// The time that the DataSource was created. The time is expressed in epoch time.
            	CreatedAt *time.Time
            
            	// 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.
            	CreatedByIamUser *string
            
            	// The location and name of the data in Amazon Simple Storage Service (Amazon S3)
            	// that is used by a DataSource.
            	DataLocationS3 *string
            
            	// A JSON string that represents the splitting and rearrangement requirement used
            	// when this DataSource was created.
            	DataRearrangement *string
            
            	// The total number of observations contained in the data files that the DataSource
            	// references.
            	DataSizeInBytes *int64
            
            	// The ID that is assigned to the DataSource during creation.
            	DataSourceId *string
            
            	// A timestamp represented in epoch time.
            	FinishedAt *time.Time
            
            	// The time of the most recent edit to the BatchPrediction. The time is expressed
            	// in epoch time.
            	LastUpdatedAt *time.Time
            
            	// A description of the most recent details about creating the DataSource.
            	Message *string
            
            	// A user-supplied name or description of the DataSource.
            	Name *string
            
            	// The number of data files referenced by the DataSource.
            	NumberOfFiles *int64
            
            	// The datasource details that are specific to Amazon RDS.
            	RDSMetadata *RDSMetadata
            
            	// Describes the DataSource details specific to Amazon Redshift.
            	RedshiftMetadata *RedshiftMetadata
            
            	// The Amazon Resource Name (ARN) of an AWS IAM Role
            	// (https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts),
            	// such as the following: arn:aws:iam::account:role/rolename.
            	RoleARN *string
            
            	// A timestamp represented in epoch time.
            	StartedAt *time.Time
            
            	// 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.
            	Status EntityStatus
            }

              Represents the output of the GetDataSource operation. The content consists of the detailed metadata and data file information and the current status of the DataSource.

              type DataSourceFilterVariable

              type DataSourceFilterVariable string
              const (
              	DataSourceFilterVariableCreatedAt     DataSourceFilterVariable = "CreatedAt"
              	DataSourceFilterVariableLastUpdatedAt DataSourceFilterVariable = "LastUpdatedAt"
              	DataSourceFilterVariableStatus        DataSourceFilterVariable = "Status"
              	DataSourceFilterVariableName          DataSourceFilterVariable = "Name"
              	DataSourceFilterVariableDataUri       DataSourceFilterVariable = "DataLocationS3"
              	DataSourceFilterVariableIamUser       DataSourceFilterVariable = "IAMUser"
              )

                Enum values for DataSourceFilterVariable

                func (DataSourceFilterVariable) Values

                  Values returns all known values for DataSourceFilterVariable. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                  type DetailsAttributes

                  type DetailsAttributes string
                  const (
                  	DetailsAttributesPredictiveModelType DetailsAttributes = "PredictiveModelType"
                  	DetailsAttributesAlgorithm           DetailsAttributes = "Algorithm"
                  )

                    Enum values for DetailsAttributes

                    func (DetailsAttributes) Values

                      Values returns all known values for DetailsAttributes. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                      type EntityStatus

                      type EntityStatus string
                      const (
                      	EntityStatusPending    EntityStatus = "PENDING"
                      	EntityStatusInprogress EntityStatus = "INPROGRESS"
                      	EntityStatusFailed     EntityStatus = "FAILED"
                      	EntityStatusCompleted  EntityStatus = "COMPLETED"
                      	EntityStatusDeleted    EntityStatus = "DELETED"
                      )

                        Enum values for EntityStatus

                        func (EntityStatus) Values

                        func (EntityStatus) Values() []EntityStatus

                          Values returns all known values for EntityStatus. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                          type Evaluation

                          type Evaluation struct {
                          
                          	// Long integer type that is a 64-bit signed number.
                          	ComputeTime *int64
                          
                          	// The time that the Evaluation was created. The time is expressed in epoch time.
                          	CreatedAt *time.Time
                          
                          	// 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.
                          	CreatedByIamUser *string
                          
                          	// The ID of the DataSource that is used to evaluate the MLModel.
                          	EvaluationDataSourceId *string
                          
                          	// The ID that is assigned to the Evaluation at creation.
                          	EvaluationId *string
                          
                          	// A timestamp represented in epoch time.
                          	FinishedAt *time.Time
                          
                          	// The location and name of the data in Amazon Simple Storage Server (Amazon S3)
                          	// that is used in the evaluation.
                          	InputDataLocationS3 *string
                          
                          	// The time of the most recent edit to the Evaluation. The time is expressed in
                          	// epoch time.
                          	LastUpdatedAt *time.Time
                          
                          	// The ID of the MLModel that is the focus of the evaluation.
                          	MLModelId *string
                          
                          	// A description of the most recent details about evaluating the MLModel.
                          	Message *string
                          
                          	// A user-supplied name or description of the Evaluation.
                          	Name *string
                          
                          	// 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
                          	// (https://docs.aws.amazon.com/machine-learning/latest/dg).
                          	PerformanceMetrics *PerformanceMetrics
                          
                          	// A timestamp represented in epoch time.
                          	StartedAt *time.Time
                          
                          	// 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.
                          	Status EntityStatus
                          }

                            Represents the output of GetEvaluation operation. The content consists of the detailed metadata and data file information and the current status of the Evaluation.

                            type EvaluationFilterVariable

                            type EvaluationFilterVariable string
                            const (
                            	EvaluationFilterVariableCreatedAt     EvaluationFilterVariable = "CreatedAt"
                            	EvaluationFilterVariableLastUpdatedAt EvaluationFilterVariable = "LastUpdatedAt"
                            	EvaluationFilterVariableStatus        EvaluationFilterVariable = "Status"
                            	EvaluationFilterVariableName          EvaluationFilterVariable = "Name"
                            	EvaluationFilterVariableIamUser       EvaluationFilterVariable = "IAMUser"
                            	EvaluationFilterVariableMlModelId     EvaluationFilterVariable = "MLModelId"
                            	EvaluationFilterVariableDatasourceId  EvaluationFilterVariable = "DataSourceId"
                            	EvaluationFilterVariableDataUri       EvaluationFilterVariable = "DataURI"
                            )

                              Enum values for EvaluationFilterVariable

                              func (EvaluationFilterVariable) Values

                                Values returns all known values for EvaluationFilterVariable. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                                type IdempotentParameterMismatchException

                                type IdempotentParameterMismatchException struct {
                                	Message *string
                                
                                	Code int32
                                }

                                  A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

                                  func (*IdempotentParameterMismatchException) Error

                                  func (*IdempotentParameterMismatchException) ErrorCode

                                  func (*IdempotentParameterMismatchException) ErrorFault

                                  func (*IdempotentParameterMismatchException) ErrorMessage

                                  func (e *IdempotentParameterMismatchException) ErrorMessage() string

                                  type InternalServerException

                                  type InternalServerException struct {
                                  	Message *string
                                  
                                  	Code int32
                                  }

                                    An error on the server occurred when trying to process a request.

                                    func (*InternalServerException) Error

                                    func (e *InternalServerException) Error() string

                                    func (*InternalServerException) ErrorCode

                                    func (e *InternalServerException) ErrorCode() string

                                    func (*InternalServerException) ErrorFault

                                    func (e *InternalServerException) ErrorFault() smithy.ErrorFault

                                    func (*InternalServerException) ErrorMessage

                                    func (e *InternalServerException) ErrorMessage() string

                                    type InvalidInputException

                                    type InvalidInputException struct {
                                    	Message *string
                                    
                                    	Code int32
                                    }

                                      An error on the client occurred. Typically, the cause is an invalid input value.

                                      func (*InvalidInputException) Error

                                      func (e *InvalidInputException) Error() string

                                      func (*InvalidInputException) ErrorCode

                                      func (e *InvalidInputException) ErrorCode() string

                                      func (*InvalidInputException) ErrorFault

                                      func (e *InvalidInputException) ErrorFault() smithy.ErrorFault

                                      func (*InvalidInputException) ErrorMessage

                                      func (e *InvalidInputException) ErrorMessage() string

                                      type InvalidTagException

                                      type InvalidTagException struct {
                                      	Message *string
                                      }

                                        A submitted tag is invalid.

                                        func (*InvalidTagException) Error

                                        func (e *InvalidTagException) Error() string

                                        func (*InvalidTagException) ErrorCode

                                        func (e *InvalidTagException) ErrorCode() string

                                        func (*InvalidTagException) ErrorFault

                                        func (e *InvalidTagException) ErrorFault() smithy.ErrorFault

                                        func (*InvalidTagException) ErrorMessage

                                        func (e *InvalidTagException) ErrorMessage() string

                                        type LimitExceededException

                                        type LimitExceededException struct {
                                        	Message *string
                                        
                                        	Code int32
                                        }

                                          The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

                                          func (*LimitExceededException) Error

                                          func (e *LimitExceededException) Error() string

                                          func (*LimitExceededException) ErrorCode

                                          func (e *LimitExceededException) ErrorCode() string

                                          func (*LimitExceededException) ErrorFault

                                          func (e *LimitExceededException) ErrorFault() smithy.ErrorFault

                                          func (*LimitExceededException) ErrorMessage

                                          func (e *LimitExceededException) ErrorMessage() string

                                          type MLModel

                                          type MLModel struct {
                                          
                                          	// 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.
                                          	Algorithm Algorithm
                                          
                                          	// Long integer type that is a 64-bit signed number.
                                          	ComputeTime *int64
                                          
                                          	// The time that the MLModel was created. The time is expressed in epoch time.
                                          	CreatedAt *time.Time
                                          
                                          	// 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.
                                          	CreatedByIamUser *string
                                          
                                          	// The current endpoint of the MLModel.
                                          	EndpointInfo *RealtimeEndpointInfo
                                          
                                          	// A timestamp represented in epoch time.
                                          	FinishedAt *time.Time
                                          
                                          	// The location of the data file or directory in Amazon Simple Storage Service
                                          	// (Amazon S3).
                                          	InputDataLocationS3 *string
                                          
                                          	// The time of the most recent edit to the MLModel. The time is expressed in epoch
                                          	// time.
                                          	LastUpdatedAt *time.Time
                                          
                                          	// The ID assigned to the MLModel at creation.
                                          	MLModelId *string
                                          
                                          	// 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?".
                                          	MLModelType MLModelType
                                          
                                          	// A description of the most recent details about accessing the MLModel.
                                          	Message *string
                                          
                                          	// A user-supplied name or description of the MLModel.
                                          	Name *string
                                          
                                          	// The score threshold for the MLModel.
                                          	ScoreThreshold *float32
                                          
                                          	// The time of the most recent edit to the ScoreThreshold. The time is expressed in
                                          	// epoch time.
                                          	ScoreThresholdLastUpdatedAt *time.Time
                                          
                                          	// Long integer type that is a 64-bit signed number.
                                          	SizeInBytes *int64
                                          
                                          	// A timestamp represented in epoch time.
                                          	StartedAt *time.Time
                                          
                                          	// 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.
                                          	Status EntityStatus
                                          
                                          	// The ID of the training DataSource. The CreateMLModel operation uses the
                                          	// TrainingDataSourceId.
                                          	TrainingDataSourceId *string
                                          
                                          	// 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 100. 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.
                                          	TrainingParameters map[string]string
                                          }

                                            Represents the output of a GetMLModel operation. The content consists of the detailed metadata and the current status of the MLModel.

                                            type MLModelFilterVariable

                                            type MLModelFilterVariable string
                                            const (
                                            	MLModelFilterVariableCreatedAt              MLModelFilterVariable = "CreatedAt"
                                            	MLModelFilterVariableLastUpdatedAt          MLModelFilterVariable = "LastUpdatedAt"
                                            	MLModelFilterVariableStatus                 MLModelFilterVariable = "Status"
                                            	MLModelFilterVariableName                   MLModelFilterVariable = "Name"
                                            	MLModelFilterVariableIamUser                MLModelFilterVariable = "IAMUser"
                                            	MLModelFilterVariableTrainingDatasourceId   MLModelFilterVariable = "TrainingDataSourceId"
                                            	MLModelFilterVariableRealTimeEndpointStatus MLModelFilterVariable = "RealtimeEndpointStatus"
                                            	MLModelFilterVariableMlModelType            MLModelFilterVariable = "MLModelType"
                                            	MLModelFilterVariableAlgorithm              MLModelFilterVariable = "Algorithm"
                                            	MLModelFilterVariableTrainingDataUri        MLModelFilterVariable = "TrainingDataURI"
                                            )

                                              Enum values for MLModelFilterVariable

                                              func (MLModelFilterVariable) Values

                                                Values returns all known values for MLModelFilterVariable. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                                                type MLModelType

                                                type MLModelType string
                                                const (
                                                	MLModelTypeRegression MLModelType = "REGRESSION"
                                                	MLModelTypeBinary     MLModelType = "BINARY"
                                                	MLModelTypeMulticlass MLModelType = "MULTICLASS"
                                                )

                                                  Enum values for MLModelType

                                                  func (MLModelType) Values

                                                  func (MLModelType) Values() []MLModelType

                                                    Values returns all known values for MLModelType. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                                                    type PerformanceMetrics

                                                    type PerformanceMetrics struct {
                                                    
                                                    	// Specific performance metric information.
                                                    	Properties map[string]string
                                                    }

                                                      Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

                                                      * BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

                                                      * RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

                                                      * MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

                                                      For more information about performance metrics, please see the Amazon Machine Learning Developer Guide (https://docs.aws.amazon.com/machine-learning/latest/dg).

                                                      type Prediction

                                                      type Prediction struct {
                                                      
                                                      	// Provides any additional details regarding the prediction.
                                                      	Details map[string]string
                                                      
                                                      	// The prediction label for either a BINARY or MULTICLASSMLModel.
                                                      	PredictedLabel *string
                                                      
                                                      	// Provides the raw classification score corresponding to each label.
                                                      	PredictedScores map[string]float32
                                                      
                                                      	// The prediction value for REGRESSIONMLModel.
                                                      	PredictedValue *float32
                                                      }

                                                        The output from a Predict operation:

                                                        * Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

                                                        * PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

                                                        * PredictedScores - Contains the raw classification score corresponding to each label.

                                                        * PredictedValue - Present for a REGRESSIONMLModel request.

                                                        type PredictorNotMountedException

                                                        type PredictorNotMountedException struct {
                                                        	Message *string
                                                        }

                                                          The exception is thrown when a predict request is made to an unmounted MLModel.

                                                          func (*PredictorNotMountedException) Error

                                                          func (*PredictorNotMountedException) ErrorCode

                                                          func (e *PredictorNotMountedException) ErrorCode() string

                                                          func (*PredictorNotMountedException) ErrorFault

                                                          func (*PredictorNotMountedException) ErrorMessage

                                                          func (e *PredictorNotMountedException) ErrorMessage() string

                                                          type RDSDataSpec

                                                          type RDSDataSpec struct {
                                                          
                                                          	// The AWS Identity and Access Management (IAM) credentials that are used connect
                                                          	// to the Amazon RDS database.
                                                          	//
                                                          	// This member is required.
                                                          	DatabaseCredentials *RDSDatabaseCredentials
                                                          
                                                          	// Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
                                                          	//
                                                          	// This member is required.
                                                          	DatabaseInformation *RDSDatabase
                                                          
                                                          	// 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
                                                          	// (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
                                                          	// for data pipelines.
                                                          	//
                                                          	// This member is required.
                                                          	ResourceRole *string
                                                          
                                                          	// The Amazon S3 location for staging Amazon RDS data. The data retrieved from
                                                          	// Amazon RDS using SelectSqlQuery is stored in this location.
                                                          	//
                                                          	// This member is required.
                                                          	S3StagingLocation *string
                                                          
                                                          	// 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.
                                                          	//
                                                          	// This member is required.
                                                          	SecurityGroupIds []string
                                                          
                                                          	// The query that is used to retrieve the observation data for the DataSource.
                                                          	//
                                                          	// This member is required.
                                                          	SelectSqlQuery *string
                                                          
                                                          	// 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
                                                          	// (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
                                                          	// for data pipelines.
                                                          	//
                                                          	// This member is required.
                                                          	ServiceRole *string
                                                          
                                                          	// 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.
                                                          	//
                                                          	// This member is required.
                                                          	SubnetId *string
                                                          
                                                          	// 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", "strategyParams": {"randomSeed":"RANDOMSEED"}}} Datasource
                                                          	// for training: {"splitting":{"percentBegin":70, "percentEnd":100,
                                                          	// "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"},
                                                          	// "complement":"true"}}
                                                          	DataRearrangement *string
                                                          
                                                          	// 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 excludedAttributeNames 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",
                                                          	// "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader":
                                                          	// true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, {
                                                          	// "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3",
                                                          	// "attributeType": "CATEGORICAL" }, { "attributeName": "F4", "attributeType":
                                                          	// "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, {
                                                          	// "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7",
                                                          	// "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8",
                                                          	// "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [
                                                          	// "F6" ] }
                                                          	DataSchema *string
                                                          
                                                          	// The Amazon S3 location of the DataSchema.
                                                          	DataSchemaUri *string
                                                          }

                                                            The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

                                                            type RDSDatabase

                                                            type RDSDatabase struct {
                                                            
                                                            	// The name of a database hosted on an RDS DB instance.
                                                            	//
                                                            	// This member is required.
                                                            	DatabaseName *string
                                                            
                                                            	// The ID of an RDS DB instance.
                                                            	//
                                                            	// This member is required.
                                                            	InstanceIdentifier *string
                                                            }

                                                              The database details of an Amazon RDS database.

                                                              type RDSDatabaseCredentials

                                                              type RDSDatabaseCredentials struct {
                                                              
                                                              	// The password to be used by Amazon ML to connect to a database on an RDS DB
                                                              	// instance. The password should have sufficient permissions to execute the
                                                              	// RDSSelectQuery query.
                                                              	//
                                                              	// This member is required.
                                                              	Password *string
                                                              
                                                              	// The username to be used by Amazon ML to connect to database on an Amazon RDS
                                                              	// instance. The username should have sufficient permissions to execute an
                                                              	// RDSSelectSqlQuery query.
                                                              	//
                                                              	// This member is required.
                                                              	Username *string
                                                              }

                                                                The database credentials to connect to a database on an RDS DB instance.

                                                                type RDSMetadata

                                                                type RDSMetadata struct {
                                                                
                                                                	// 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.
                                                                	DataPipelineId *string
                                                                
                                                                	// The database details required to connect to an Amazon RDS.
                                                                	Database *RDSDatabase
                                                                
                                                                	// The username to be used by Amazon ML to connect to database on an Amazon RDS
                                                                	// instance. The username should have sufficient permissions to execute an
                                                                	// RDSSelectSqlQuery query.
                                                                	DatabaseUserName *string
                                                                
                                                                	// 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
                                                                	// (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
                                                                	// for data pipelines.
                                                                	ResourceRole *string
                                                                
                                                                	// The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if
                                                                	// Verbose is true in GetDataSourceInput.
                                                                	SelectSqlQuery *string
                                                                
                                                                	// 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
                                                                	// (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
                                                                	// for data pipelines.
                                                                	ServiceRole *string
                                                                }

                                                                  The datasource details that are specific to Amazon RDS.

                                                                  type RealtimeEndpointInfo

                                                                  type RealtimeEndpointInfo struct {
                                                                  
                                                                  	// The time that the request to create the real-time endpoint for the MLModel was
                                                                  	// received. The time is expressed in epoch time.
                                                                  	CreatedAt *time.Time
                                                                  
                                                                  	// 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.
                                                                  	EndpointStatus RealtimeEndpointStatus
                                                                  
                                                                  	// 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.
                                                                  	EndpointUrl *string
                                                                  
                                                                  	// The maximum processing rate for the real-time endpoint for MLModel, measured in
                                                                  	// incoming requests per second.
                                                                  	PeakRequestsPerSecond int32
                                                                  }

                                                                    Describes the real-time endpoint information for an MLModel.

                                                                    type RealtimeEndpointStatus

                                                                    type RealtimeEndpointStatus string
                                                                    const (
                                                                    	RealtimeEndpointStatusNone     RealtimeEndpointStatus = "NONE"
                                                                    	RealtimeEndpointStatusReady    RealtimeEndpointStatus = "READY"
                                                                    	RealtimeEndpointStatusUpdating RealtimeEndpointStatus = "UPDATING"
                                                                    	RealtimeEndpointStatusFailed   RealtimeEndpointStatus = "FAILED"
                                                                    )

                                                                      Enum values for RealtimeEndpointStatus

                                                                      func (RealtimeEndpointStatus) Values

                                                                        Values returns all known values for RealtimeEndpointStatus. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                                                                        type RedshiftDataSpec

                                                                        type RedshiftDataSpec struct {
                                                                        
                                                                        	// Describes AWS Identity and Access Management (IAM) credentials that are used
                                                                        	// connect to the Amazon Redshift database.
                                                                        	//
                                                                        	// This member is required.
                                                                        	DatabaseCredentials *RedshiftDatabaseCredentials
                                                                        
                                                                        	// Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift
                                                                        	// DataSource.
                                                                        	//
                                                                        	// This member is required.
                                                                        	DatabaseInformation *RedshiftDatabase
                                                                        
                                                                        	// Describes an Amazon S3 location to store the result set of the SelectSqlQuery
                                                                        	// query.
                                                                        	//
                                                                        	// This member is required.
                                                                        	S3StagingLocation *string
                                                                        
                                                                        	// Describes the SQL Query to execute on an Amazon Redshift database for an Amazon
                                                                        	// Redshift DataSource.
                                                                        	//
                                                                        	// This member is required.
                                                                        	SelectSqlQuery *string
                                                                        
                                                                        	// 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", "strategyParams": {"randomSeed":"RANDOMSEED"}}} Datasource
                                                                        	// for training: {"splitting":{"percentBegin":70, "percentEnd":100,
                                                                        	// "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"},
                                                                        	// "complement":"true"}}
                                                                        	DataRearrangement *string
                                                                        
                                                                        	// 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 excludedAttributeNames 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",
                                                                        	// "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader":
                                                                        	// true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, {
                                                                        	// "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3",
                                                                        	// "attributeType": "CATEGORICAL" }, { "attributeName": "F4", "attributeType":
                                                                        	// "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, {
                                                                        	// "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7",
                                                                        	// "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8",
                                                                        	// "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [
                                                                        	// "F6" ] }
                                                                        	DataSchema *string
                                                                        
                                                                        	// Describes the schema location for an Amazon Redshift DataSource.
                                                                        	DataSchemaUri *string
                                                                        }

                                                                          Describes the data specification of an Amazon Redshift DataSource.

                                                                          type RedshiftDatabase

                                                                          type RedshiftDatabase struct {
                                                                          
                                                                          	// The ID of an Amazon Redshift cluster.
                                                                          	//
                                                                          	// This member is required.
                                                                          	ClusterIdentifier *string
                                                                          
                                                                          	// The name of a database hosted on an Amazon Redshift cluster.
                                                                          	//
                                                                          	// This member is required.
                                                                          	DatabaseName *string
                                                                          }

                                                                            Describes the database details required to connect to an Amazon Redshift database.

                                                                            type RedshiftDatabaseCredentials

                                                                            type RedshiftDatabaseCredentials struct {
                                                                            
                                                                            	// A password to be used by Amazon ML to connect to a database on an Amazon
                                                                            	// Redshift cluster. The password should have sufficient permissions to execute a
                                                                            	// RedshiftSelectSqlQuery query. The password should be valid for an Amazon
                                                                            	// Redshift USER
                                                                            	// (https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
                                                                            	//
                                                                            	// This member is required.
                                                                            	Password *string
                                                                            
                                                                            	// A username to be used by Amazon Machine Learning (Amazon ML)to connect to a
                                                                            	// database on an Amazon Redshift cluster. The username should have sufficient
                                                                            	// permissions to execute the RedshiftSelectSqlQuery query. The username should be
                                                                            	// valid for an Amazon Redshift USER
                                                                            	// (https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
                                                                            	//
                                                                            	// This member is required.
                                                                            	Username *string
                                                                            }

                                                                              Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

                                                                              type RedshiftMetadata

                                                                              type RedshiftMetadata struct {
                                                                              
                                                                              	// A username to be used by Amazon Machine Learning (Amazon ML)to connect to a
                                                                              	// database on an Amazon Redshift cluster. The username should have sufficient
                                                                              	// permissions to execute the RedshiftSelectSqlQuery query. The username should be
                                                                              	// valid for an Amazon Redshift USER
                                                                              	// (https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
                                                                              	DatabaseUserName *string
                                                                              
                                                                              	// Describes the database details required to connect to an Amazon Redshift
                                                                              	// database.
                                                                              	RedshiftDatabase *RedshiftDatabase
                                                                              
                                                                              	// The SQL query that is specified during CreateDataSourceFromRedshift. Returns
                                                                              	// only if Verbose is true in GetDataSourceInput.
                                                                              	SelectSqlQuery *string
                                                                              }

                                                                                Describes the DataSource details specific to Amazon Redshift.

                                                                                type ResourceNotFoundException

                                                                                type ResourceNotFoundException struct {
                                                                                	Message *string
                                                                                
                                                                                	Code int32
                                                                                }

                                                                                  A specified resource cannot be located.

                                                                                  func (*ResourceNotFoundException) Error

                                                                                  func (e *ResourceNotFoundException) Error() string

                                                                                  func (*ResourceNotFoundException) ErrorCode

                                                                                  func (e *ResourceNotFoundException) ErrorCode() string

                                                                                  func (*ResourceNotFoundException) ErrorFault

                                                                                  func (*ResourceNotFoundException) ErrorMessage

                                                                                  func (e *ResourceNotFoundException) ErrorMessage() string

                                                                                  type S3DataSpec

                                                                                  type S3DataSpec struct {
                                                                                  
                                                                                  	// 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.
                                                                                  	//
                                                                                  	// This member is required.
                                                                                  	DataLocationS3 *string
                                                                                  
                                                                                  	// 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", "strategyParams": { "randomSeed":"RANDOMSEED"}}} Datasource
                                                                                  	// for training: {"splitting":{"percentBegin":70, "percentEnd":100,
                                                                                  	// "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"},
                                                                                  	// "complement":"true"}}
                                                                                  	DataRearrangement *string
                                                                                  
                                                                                  	// 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 excludedAttributeNames 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",
                                                                                  	// "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader":
                                                                                  	// true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, {
                                                                                  	// "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3",
                                                                                  	// "attributeType": "CATEGORICAL" }, { "attributeName": "F4", "attributeType":
                                                                                  	// "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, {
                                                                                  	// "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7",
                                                                                  	// "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8",
                                                                                  	// "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [
                                                                                  	// "F6" ] }
                                                                                  	DataSchema *string
                                                                                  
                                                                                  	// Describes the schema location in Amazon S3. You must provide either the
                                                                                  	// DataSchema or the DataSchemaLocationS3.
                                                                                  	DataSchemaLocationS3 *string
                                                                                  }

                                                                                    Describes the data specification of a DataSource.

                                                                                    type SortOrder

                                                                                    type SortOrder string
                                                                                    const (
                                                                                    	SortOrderAsc SortOrder = "asc"
                                                                                    	SortOrderDsc SortOrder = "dsc"
                                                                                    )

                                                                                      Enum values for SortOrder

                                                                                      func (SortOrder) Values

                                                                                      func (SortOrder) Values() []SortOrder

                                                                                        Values returns all known values for SortOrder. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.

                                                                                        type Tag

                                                                                        type Tag struct {
                                                                                        
                                                                                        	// A unique identifier for the tag. Valid characters include Unicode letters,
                                                                                        	// digits, white space, _, ., /, =, +, -, %, and @.
                                                                                        	Key *string
                                                                                        
                                                                                        	// An optional string, typically used to describe or define the tag. Valid
                                                                                        	// characters include Unicode letters, digits, white space, _, ., /, =, +, -, %,
                                                                                        	// and @.
                                                                                        	Value *string
                                                                                        }

                                                                                          A custom key-value pair associated with an ML object, such as an ML model.

                                                                                          type TagLimitExceededException

                                                                                          type TagLimitExceededException struct {
                                                                                          	Message *string
                                                                                          }

                                                                                            The limit in the number of tags has been exceeded.

                                                                                            func (*TagLimitExceededException) Error

                                                                                            func (e *TagLimitExceededException) Error() string

                                                                                            func (*TagLimitExceededException) ErrorCode

                                                                                            func (e *TagLimitExceededException) ErrorCode() string

                                                                                            func (*TagLimitExceededException) ErrorFault

                                                                                            func (*TagLimitExceededException) ErrorMessage

                                                                                            func (e *TagLimitExceededException) ErrorMessage() string

                                                                                            type TaggableResourceType

                                                                                            type TaggableResourceType string
                                                                                            const (
                                                                                            	TaggableResourceTypeBatchPrediction TaggableResourceType = "BatchPrediction"
                                                                                            	TaggableResourceTypeDatasource      TaggableResourceType = "DataSource"
                                                                                            	TaggableResourceTypeEvaluation      TaggableResourceType = "Evaluation"
                                                                                            	TaggableResourceTypeMlModel         TaggableResourceType = "MLModel"
                                                                                            )

                                                                                              Enum values for TaggableResourceType

                                                                                              func (TaggableResourceType) Values

                                                                                                Values returns all known values for TaggableResourceType. Note that this can be expanded in the future, and so it is only as up to date as the client. The ordering of this slice is not guaranteed to be stable across updates.