machinelearning

package
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 Go to latest Published: Jun 16, 2015 License: Apache-2.0 Imports: 5 Imported by: 0

Documentation

Overview

Package machinelearning provides a client for Amazon Machine Learning.

Index

Examples

Constants

This section is empty.

Variables

This section is empty.

Functions

This section is empty.

Types

type BatchPrediction 

type BatchPrediction struct {
	// The ID of the DataSource that points to the group of observations to predict.
	BatchPredictionDataSourceID *string `locationName:"BatchPredictionDataSourceId" type:"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 `locationName:"BatchPredictionId" type:"string"`

	// The time that the BatchPrediction was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID of the MLModel that generated predictions for the BatchPrediction
	// request.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// A description of the most recent details about processing the batch prediction
	// request.
	Message *string `type:"string"`

	// A user-supplied name or description of the BatchPrediction.
	Name *string `type:"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 `locationName:"OutputUri" type:"string"`

	// 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 peform 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 *string `type:"string"`
	// contains filtered or unexported fields
}

Represents the output of GetBatchPrediction operation. 

The content consists of the detailed metadata, the status, and the data

file information of a Batch Prediction.

type CreateBatchPredictionInput 

type CreateBatchPredictionInput struct {
	// The ID of the DataSource that points to the group of observations to predict.
	BatchPredictionDataSourceID *string `locationName:"BatchPredictionDataSourceId" type:"string" required:"true"`

	// A user-supplied ID that uniquely identifies the BatchPrediction.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string" required:"true"`

	// A user-supplied name or description of the BatchPrediction. BatchPredictionName
	// can only use the UTF-8 character set.
	BatchPredictionName *string `type:"string"`

	// The ID of the MLModel that will generate predictions for the group of observations.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`

	// The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory
	// to store the batch prediction results. The following substrings are not allowed
	// in the s3 key portion of the "outputURI" field: ':', '//', '/./', '/../'.
	//
	// Amazon ML needs permissions to store and retrieve the logs on your behalf.
	// For information about how to set permissions, see the Amazon Machine Learning
	// Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	OutputURI *string `locationName:"OutputUri" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateBatchPredictionOutput 

type CreateBatchPredictionOutput struct {
	// A user-supplied ID that uniquely identifies the BatchPrediction. This value
	// is identical to the value of the BatchPredictionId in the request.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.

The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result.

type CreateDataSourceFromRDSInput 

type CreateDataSourceFromRDSInput struct {
	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during an MLModel training. This parameter must be set to true
	// if the DataSource needs to be used for MLModel training.
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied ID that uniquely identifies the DataSource. Typically, an
	// Amazon Resource Number (ARN) becomes the ID for a DataSource.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of an Amazon RDS DataSource:
	//
	//  DatabaseInformation -   DatabaseName  - Name of the Amazon RDS database.
	//   InstanceIdentifier  - Unique identifier for the Amazon RDS database instance.
	//
	//
	// DatabaseCredentials - AWS Identity and Access Management (IAM) credentials
	// that are used to connect to the Amazon RDS database.
	//
	// ResourceRole - Role (DataPipelineDefaultResourceRole) assumed by an Amazon
	// Elastic Compute Cloud (EC2) instance to carry out the copy task from Amazon
	// RDS to Amazon S3. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline
	// service to monitor the progress of the copy task from Amazon RDS to Amazon
	// Simple Storage Service (S3). For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// SecurityInfo - Security information to use to access an Amazon RDS instance.
	// You need to set up appropriate ingress rules for the security entity IDs
	// provided to allow access to the Amazon RDS instance. Specify a [SubnetId,
	// SecurityGroupIds] pair for a VPC-based Amazon RDS instance.
	//
	// SelectSqlQuery - Query that is used to retrieve the observation data for
	// the Datasource.
	//
	// S3StagingLocation - Amazon S3 location for staging RDS data. The data retrieved
	// from Amazon RDS using SelectSqlQuery is stored in this location.
	//
	// DataSchemaUri - Amazon S3 location of the DataSchema.
	//
	// DataSchema - A JSON string representing the schema. This is not required
	// if DataSchemaUri is specified.
	//
	//  DataRearrangement - A JSON string representing the splitting requirement
	// of a Datasource.
	//
	//   Sample -  "{\"randomSeed\":\"some-random-seed\", \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	RDSData *RDSDataSpec `type:"structure" required:"true"`

	// The role that Amazon ML assumes on behalf of the user to create and activate
	// a data pipeline in the user’s account and copy data (using the SelectSqlQuery)
	// query from Amazon RDS to Amazon S3.
	RoleARN *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateDataSourceFromRDSOutput 

type CreateDataSourceFromRDSOutput struct {
	// A user-supplied ID that uniquely identifies the datasource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRDS operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

type CreateDataSourceFromRedshiftInput 

type CreateDataSourceFromRedshiftInput struct {
	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during MLModel training. This parameter must be set to true if
	// the DataSource needs to be used for MLModel training
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied ID that uniquely identifies the DataSource.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of an Amazon Redshift DataSource:
	//
	//  DatabaseInformation -   DatabaseName  - Name of the Amazon Redshift database.
	//    ClusterIdentifier  - Unique ID for the Amazon Redshift cluster.
	//
	// DatabaseCredentials - AWS Identity abd Access Management (IAM) credentials
	// that are used to connect to the Amazon Redshift database.
	//
	// SelectSqlQuery - Query that is used to retrieve the observation data for
	// the Datasource.
	//
	// S3StagingLocation - Amazon Simple Storage Service (Amazon S3) location for
	// staging Amazon Redshift data. The data retrieved from Amazon Relational Database
	// Service (Amazon RDS) using SelectSqlQuery is stored in this location.
	//
	// DataSchemaUri - Amazon S3 location of the DataSchema.
	//
	// DataSchema - A JSON string representing the schema. This is not required
	// if DataSchemaUri is specified.
	//
	//  DataRearrangement - A JSON string representing the splitting requirement
	// of a Datasource.
	//
	//   Sample -  "{\"randomSeed\":\"some-random-seed\", \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	DataSpec *RedshiftDataSpec `type:"structure" required:"true"`

	// A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the
	// role on behalf of the user to create the following:
	//
	//   A security group to allow Amazon ML to execute the SelectSqlQuery query
	// on an Amazon Redshift cluster
	//
	// An Amazon S3 bucket policy to grant Amazon ML read/write permissions on
	// the S3StagingLocation
	RoleARN *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateDataSourceFromRedshiftOutput 

type CreateDataSourceFromRedshiftOutput struct {
	// A user-supplied ID that uniquely identifies the datasource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

type CreateDataSourceFromS3Input 

type CreateDataSourceFromS3Input struct {
	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during an MLModel training. This parameter must be set to true
	// if the DataSource needs to be used for MLModel training
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied identifier that uniquely identifies the DataSource.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of a DataSource:
	//
	//  DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of
	// the observation data.
	//
	// DataSchemaLocationS3 - Amazon S3 location of the DataSchema.
	//
	// DataSchema - A JSON string representing the schema. This is not required
	// if DataSchemaUri is specified.
	//
	//  DataRearrangement - A JSON string representing the splitting requirement
	// of a Datasource.
	//
	//   Sample -  "{\"randomSeed\":\"some-random-seed\", \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	DataSpec *S3DataSpec `type:"structure" required:"true"`
	// contains filtered or unexported fields
}

type CreateDataSourceFromS3Output 

type CreateDataSourceFromS3Output struct {
	// A user-supplied ID that uniquely identifies the datasource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

type CreateEvaluationInput 

type CreateEvaluationInput struct {
	// The ID of the DataSource for the evaluation. The schema of the DataSource
	// must match the schema used to create the MLModel.
	EvaluationDataSourceID *string `locationName:"EvaluationDataSourceId" type:"string" required:"true"`

	// A user-supplied ID that uniquely identifies the Evaluation.
	EvaluationID *string `locationName:"EvaluationId" type:"string" required:"true"`

	// A user-supplied name or description of the Evaluation.
	EvaluationName *string `type:"string"`

	// The ID of the MLModel to evaluate.
	//
	// The schema used in creating the MLModel must match the schema of the DataSource
	// used in the Evaluation.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateEvaluationOutput 

type CreateEvaluationOutput struct {
	// The user-supplied ID that uniquely identifies the Evaluation. This value
	// should be identical to the value of the EvaluationId in the request.
	EvaluationID *string `locationName:"EvaluationId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvaluation operation and checking the Status parameter.

type CreateMLModelInput 

type CreateMLModelInput struct {
	// A user-supplied ID that uniquely identifies the MLModel.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`

	// A user-supplied name or description of the MLModel.
	MLModelName *string `type:"string"`

	// The category of supervised learning that this MLModel will address. Choose
	// from the following types:
	//
	//  Choose REGRESSION if the MLModel will be used to predict a numeric value.
	// Choose BINARY if the MLModel result has two possible values. Choose MULTICLASS
	// if the MLModel result has a limited number of values.    For more information,
	// see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	MLModelType *string `type:"string" required:"true"`

	// 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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls
	// overfitting the data by penalizing large coefficients. This 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 not
	// to use L1 normalization. The parameter cannot be used when L2 is specified.
	// Use this parameter sparingly.
	//
	//   sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls
	// overfitting the data by penalizing large coefficients. This tends to drive
	// coefficients to small, nonzero values. If you use this parameter, start by
	// specifying a small value such as 1.0E-08.
	//
	// The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not
	// to use L2 normalization. This cannot be used when L1 is specified. Use this
	// parameter sparingly.
	//
	//  sgd.maxPasses - Number of times that the training process traverses the
	// observations to build the MLModel. The value is an integer that ranges from
	// 1 to 10000. The default value is 10.
	//
	//  sgd.maxMLModelSizeInBytes - 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.
	Parameters map[string]*string `type:"map"`

	// The data recipe for creating MLModel. You must specify either the recipe
	// or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a
	// default.
	Recipe *string `type:"string"`

	// The Amazon Simple Storage Service (Amazon S3) location and file name that
	// contains the MLModel recipe. You must specify either the recipe or its URI.
	// If you don’t specify a recipe or its URI, Amazon ML creates a default.
	RecipeURI *string `locationName:"RecipeUri" type:"string"`

	// The DataSource that points to the training data.
	TrainingDataSourceID *string `locationName:"TrainingDataSourceId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateMLModelOutput 

type CreateMLModelOutput struct {
	// A user-supplied ID that uniquely identifies the MLModel. This value should
	// be identical to the value of the MLModelId in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.

The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

type CreateRealtimeEndpointInput 

type CreateRealtimeEndpointInput struct {
	// The ID assigned to the MLModel during creation.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type CreateRealtimeEndpointOutput 

type CreateRealtimeEndpointOutput struct {
	// A user-supplied ID that uniquely identifies the MLModel. This value should
	// be identical to the value of the MLModelId in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// The endpoint information of the MLModel
	RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"`
	// contains filtered or unexported fields
}

Represents the output of an CreateRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel. 

The endpoint information includes the URI of the MLModel; that is, the

location to send online prediction requests for the specified MLModel.

type DataSource 

type DataSource struct {
	// The parameter is true if statistics need to be generated from the observation
	// data.
	ComputeStatistics *bool `type:"boolean"`

	// The time that the DataSource was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The location and name of the data in Amazon Simple Storage Service (Amazon
	// S3) that is used by a DataSource.
	DataLocationS3 *string `type:"string"`

	// A JSON string that represents the splitting requirement of a Datasource.
	DataRearrangement *string `type:"string"`

	// The total number of observations contained in the data files that the DataSource
	// references.
	DataSizeInBytes *int64 `type:"long"`

	// The ID that is assigned to the DataSource during creation.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`

	// The time of the most recent edit to the BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A description of the most recent details about creating the DataSource.
	Message *string `type:"string"`

	// A user-supplied name or description of the DataSource.
	Name *string `type:"string"`

	// The number of data files referenced by the DataSource.
	NumberOfFiles *int64 `type:"long"`

	// The datasource details that are specific to Amazon RDS.
	RDSMetadata *RDSMetadata `type:"structure"`

	// Describes the DataSource details specific to Amazon Redshift.
	RedshiftMetadata *RedshiftMetadata `type:"structure"`

	// The Amazon Resource Name (ARN) of an AWS IAM Role (http://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 `type:"string"`

	// 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 *string `type:"string"`
	// contains filtered or unexported fields
}

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 DeleteBatchPredictionInput 

type DeleteBatchPredictionInput struct {
	// A user-supplied ID that uniquely identifies the BatchPrediction.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type DeleteBatchPredictionOutput 

type DeleteBatchPredictionOutput struct {
	// A user-supplied ID that uniquely identifies the BatchPrediction. This value
	// should be identical to the value of the BatchPredictionID in the request.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteBatchPrediction operation.

You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

type DeleteDataSourceInput 

type DeleteDataSourceInput struct {
	// A user-supplied ID that uniquely identifies the DataSource.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type DeleteDataSourceOutput 

type DeleteDataSourceOutput struct {
	// A user-supplied ID that uniquely identifies the DataSource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteDataSource operation.

type DeleteEvaluationInput 

type DeleteEvaluationInput struct {
	// A user-supplied ID that uniquely identifies the Evaluation to delete.
	EvaluationID *string `locationName:"EvaluationId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type DeleteEvaluationOutput 

type DeleteEvaluationOutput struct {
	// A user-supplied ID that uniquely identifies the Evaluation. This value should
	// be identical to the value of the EvaluationId in the request.
	EvaluationID *string `locationName:"EvaluationId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.

You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

type DeleteMLModelInput 

type DeleteMLModelInput struct {
	// A user-supplied ID that uniquely identifies the MLModel.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type DeleteMLModelOutput 

type DeleteMLModelOutput struct {
	// A user-supplied ID that uniquely identifies the MLModel. This value should
	// be identical to the value of the MLModelID in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteMLModel operation.

You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

type DeleteRealtimeEndpointInput 

type DeleteRealtimeEndpointInput struct {
	// The ID assigned to the MLModel during creation.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type DeleteRealtimeEndpointOutput 

type DeleteRealtimeEndpointOutput struct {
	// A user-supplied ID that uniquely identifies the MLModel. This value should
	// be identical to the value of the MLModelId in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// The endpoint information of the MLModel
	RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"`
	// contains filtered or unexported fields
}

Represents the output of an DeleteRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

type DescribeBatchPredictionsInput 

type DescribeBatchPredictionsInput struct {
	// The equal to operator. The BatchPrediction results will have FilterVariable
	// values that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of BatchPrediction:
	//
	//   CreatedAt - Sets the search criteria to the BatchPrediction creation date.
	//  Status - Sets the search criteria to the BatchPrediction status.  Name -
	// Sets the search criteria to the contents of the BatchPrediction  Name.  IAMUser
	// - Sets the search criteria to the user account that invoked the BatchPrediction
	// creation.  MLModelId - Sets the search criteria to the MLModel used in the
	// BatchPrediction.  DataSourceId - Sets the search criteria to the DataSource
	// used in the BatchPrediction.  DataURI - Sets the search criteria to the data
	// file(s) used in the BatchPrediction. The URL can identify either a file or
	// an Amazon Simple Storage Solution (Amazon S3) bucket or directory.
	FilterVariable *string `type:"string"`

	// The greater than or equal to operator. The BatchPrediction results will have
	// FilterVariable values that are greater than or equal to the value specified
	// with GE.
	GE *string `type:"string"`

	// The greater than operator. The BatchPrediction results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The BatchPrediction results will have
	// FilterVariable values that are less than or equal to the value specified
	// with LE.
	LE *string `type:"string"`

	// The less than operator. The BatchPrediction results will have FilterVariable
	// values that are less than the value specified with LT.
	LT *string `type:"string"`

	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit *int64 `type:"integer"`

	// The not equal to operator. The BatchPrediction results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// An ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer.
	// To search for this BatchPrediction, select Name for the FilterVariable and
	// any of the following strings for the Prefix:
	//
	//  2014-09
	//
	// 2014-09-09
	//
	// 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of MLModels.
	//
	//   asc - Arranges the list in ascending order (A-Z, 0-9).  dsc - Arranges
	// the list in descending order (Z-A, 9-0).  Results are sorted by FilterVariable.
	SortOrder *string `type:"string"`
	// contains filtered or unexported fields
}

type DescribeBatchPredictionsOutput 

type DescribeBatchPredictionsOutput struct {
	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of BatchPrediction objects that meet the search criteria.
	Results []*BatchPrediction `type:"list"`
	// contains filtered or unexported fields
}

Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

type DescribeDataSourcesInput 

type DescribeDataSourcesInput struct {
	// The equal to operator. The DataSource results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of DataSource:
	//
	//   CreatedAt - Sets the search criteria to DataSource creation dates.  Status
	// - Sets the search criteria to DataSource statuses.  Name - Sets the search
	// criteria to the contents of DataSource   Name.  DataUri - Sets the search
	// criteria to the URI of data files used to create the DataSource. The URI
	// can identify either a file or an Amazon Simple Storage Service (Amazon S3)
	// bucket or directory.  IAMUser - Sets the search criteria to the user account
	// that invoked the DataSource creation.
	FilterVariable *string `type:"string"`

	// The greater than or equal to operator. The DataSource results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The DataSource results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The DataSource results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The DataSource results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The maximum number of DataSource to include in the result.
	Limit *int64 `type:"integer"`

	// The not equal to operator. The DataSource results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, a DataSource could have the Name 2014-09-09-HolidayGiftMailer.
	// To search for this DataSource, select Name for the FilterVariable and any
	// of the following strings for the Prefix:
	//
	//  2014-09
	//
	// 2014-09-09
	//
	// 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of DataSource.
	//
	//   asc - Arranges the list in ascending order (A-Z, 0-9).  dsc - Arranges
	// the list in descending order (Z-A, 9-0).  Results are sorted by FilterVariable.
	SortOrder *string `type:"string"`
	// contains filtered or unexported fields
}

type DescribeDataSourcesOutput 

type DescribeDataSourcesOutput struct {
	// An ID of the next page in the paginated results that indicates at least one
	// more page follows.
	NextToken *string `type:"string"`

	// A list of DataSource that meet the search criteria.
	Results []*DataSource `type:"list"`
	// contains filtered or unexported fields
}

Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.

type DescribeEvaluationsInput 

type DescribeEvaluationsInput struct {
	// The equal to operator. The Evaluation results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variable to filter a list of Evaluation objects:
	//
	//   CreatedAt - Sets the search criteria to the Evaluation creation date.
	//  Status - Sets the search criteria to the Evaluation status.  Name - Sets
	// the search criteria to the contents of Evaluation   Name.  IAMUser - Sets
	// the search criteria to the user account that invoked an Evaluation.  MLModelId
	// - Sets the search criteria to the MLModel that was evaluated.  DataSourceId
	// - Sets the search criteria to the DataSource used in Evaluation.  DataUri
	// - Sets the search criteria to the data file(s) used in Evaluation. The URL
	// can identify either a file or an Amazon Simple Storage Solution (Amazon S3)
	// bucket or directory.
	FilterVariable *string `type:"string"`

	// The greater than or equal to operator. The Evaluation results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The Evaluation results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The Evaluation results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The Evaluation results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The maximum number of Evaluation to include in the result.
	Limit *int64 `type:"integer"`

	// The not equal to operator. The Evaluation results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer.
	// To search for this Evaluation, select Name for the FilterVariable and any
	// of the following strings for the Prefix:
	//
	//  2014-09
	//
	// 2014-09-09
	//
	// 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of Evaluation.
	//
	//   asc - Arranges the list in ascending order (A-Z, 0-9).  dsc - Arranges
	// the list in descending order (Z-A, 9-0).  Results are sorted by FilterVariable.
	SortOrder *string `type:"string"`
	// contains filtered or unexported fields
}

type DescribeEvaluationsOutput 

type DescribeEvaluationsOutput struct {
	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of Evaluation that meet the search criteria.
	Results []*Evaluation `type:"list"`
	// contains filtered or unexported fields
}

Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.

type DescribeMLModelsInput 

type DescribeMLModelsInput struct {
	// The equal to operator. The MLModel results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of MLModel:
	//
	//   CreatedAt - Sets the search criteria to MLModel creation date.  Status
	// - Sets the search criteria to MLModel status.  Name - Sets the search criteria
	// to the contents of MLModel  Name.  IAMUser - Sets the search criteria to
	// the user account that invoked the MLModel creation.  TrainingDataSourceId
	// - Sets the search criteria to the DataSource used to train one or more MLModel.
	//  RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time
	// endpoint status.  MLModelType - Sets the search criteria to MLModel type:
	// binary, regression, or multi-class.  Algorithm - Sets the search criteria
	// to the algorithm that the MLModel uses.  TrainingDataURI - Sets the search
	// criteria to the data file(s) used in training a MLModel. The URL can identify
	// either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
	FilterVariable *string `type:"string"`

	// The greater than or equal to operator. The MLModel results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The MLModel results will have FilterVariable values
	// that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The MLModel results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The MLModel results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit *int64 `type:"integer"`

	// The not equal to operator. The MLModel results will have FilterVariable values
	// not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer.
	// To search for this MLModel, select Name for the FilterVariable and any of
	// the following strings for the Prefix:
	//
	//  2014-09
	//
	// 2014-09-09
	//
	// 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of MLModel.
	//
	//   asc - Arranges the list in ascending order (A-Z, 0-9).  dsc - Arranges
	// the list in descending order (Z-A, 9-0).  Results are sorted by FilterVariable.
	SortOrder *string `type:"string"`
	// contains filtered or unexported fields
}

type DescribeMLModelsOutput 

type DescribeMLModelsOutput struct {
	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of MLModel that meet the search criteria.
	Results []*MLModel `type:"list"`
	// contains filtered or unexported fields
}

Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

type Evaluation 

type Evaluation struct {
	// The time that the Evaluation was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The ID of the DataSource that is used to evaluate the MLModel.
	EvaluationDataSourceID *string `locationName:"EvaluationDataSourceId" type:"string"`

	// The ID that is assigned to the Evaluation at creation.
	EvaluationID *string `locationName:"EvaluationId" type:"string"`

	// The location and name of the data in Amazon Simple Storage Server (Amazon
	// S3) that is used in the evaluation.
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the Evaluation. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID of the MLModel that is the focus of the evaluation.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// A description of the most recent details about evaluating the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the Evaluation.
	Name *string `type:"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 (http://docs.aws.amazon.com/machine-learning/latest/dg).
	PerformanceMetrics *PerformanceMetrics `type:"structure"`

	// 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 *string `type:"string"`
	// contains filtered or unexported fields
}

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 GetBatchPredictionInput 

type GetBatchPredictionInput struct {
	// An ID assigned to the BatchPrediction at creation.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type GetBatchPredictionOutput 

type GetBatchPredictionOutput struct {
	// The ID of the DataSource that was used to create the BatchPrediction.
	BatchPredictionDataSourceID *string `locationName:"BatchPredictionDataSourceId" type:"string"`

	// An ID assigned to the BatchPrediction at creation. This value should be identical
	// to the value of the BatchPredictionID in the request.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string"`

	// The time when the BatchPrediction was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file that contains logs of the CreateBatchPrediction operation.
	LogURI *string `locationName:"LogUri" type:"string"`

	// The ID of the MLModel that generated predictions for the BatchPrediction
	// request.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// A description of the most recent details about processing the batch prediction
	// request.
	Message *string `type:"string"`

	// A user-supplied name or description of the BatchPrediction.
	Name *string `type:"string"`

	// The location of an Amazon S3 bucket or directory to receive the operation
	// results.
	OutputURI *string `locationName:"OutputUri" type:"string"`

	// The status of the BatchPrediction, which can be one of the following values:
	//
	//   PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate
	// batch predictions.  INPROGRESS - The batch predictions are in progress.
	// FAILED - The request to perform a batch prediction did not run to completion.
	// It is not usable.  COMPLETED - The batch prediction process completed successfully.
	//  DELETED - The BatchPrediction is marked as deleted. It is not usable.
	Status *string `type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.

type GetDataSourceInput 

type GetDataSourceInput struct {
	// The ID assigned to the DataSource at creation.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`

	// Specifies whether the GetDataSource operation should return DataSourceSchema.
	//
	// If true, DataSourceSchema is returned.
	//
	// If false, DataSourceSchema is not returned.
	Verbose *bool `type:"boolean"`
	// contains filtered or unexported fields
}

type GetDataSourceOutput 

type GetDataSourceOutput struct {
	// The parameter is true if statistics need to be generated from the observation
	// data.
	ComputeStatistics *bool `type:"boolean"`

	// The time that the DataSource was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	DataLocationS3 *string `type:"string"`

	// A JSON string that captures the splitting rearrangement requirement of the
	// DataSource.
	DataRearrangement *string `type:"string"`

	// The total size of observations in the data files.
	DataSizeInBytes *int64 `type:"long"`

	// The ID assigned to the DataSource at creation. This value should be identical
	// to the value of the DataSourceId in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`

	// The schema used by all of the data files of this DataSource.
	//
	// Note This parameter is provided as part of the verbose format.
	DataSourceSchema *string `type:"string"`

	// The time of the most recent edit to the DataSource. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file containining logs of either create DataSource operation.
	LogURI *string `locationName:"LogUri" type:"string"`

	// The description of the most recent details about creating the DataSource.
	Message *string `type:"string"`

	// A user-supplied name or description of the DataSource.
	Name *string `type:"string"`

	// The number of data files referenced by the DataSource.
	NumberOfFiles *int64 `type:"long"`

	// The datasource details that are specific to Amazon RDS.
	RDSMetadata *RDSMetadata `type:"structure"`

	// Describes the DataSource details specific to Amazon Redshift.
	RedshiftMetadata *RedshiftMetadata `type:"structure"`

	// The Amazon Resource Name (ARN) of an AWS IAM Role (http://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 `type:"string"`

	// The current status of the DataSource. This element can have one of the following
	// values:
	//
	//   PENDING - Amazon Machine Language (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 *string `type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a GetDataSource operation and describes a DataSource.

type GetEvaluationInput 

type GetEvaluationInput struct {
	// The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded
	// and cataloged. The ID provides the means to access the information.
	EvaluationID *string `locationName:"EvaluationId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

type GetEvaluationOutput 

type GetEvaluationOutput struct {
	// The time that the Evaluation was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The DataSource used for this evaluation.
	EvaluationDataSourceID *string `locationName:"EvaluationDataSourceId" type:"string"`

	// The evaluation ID which is same as the EvaluationId in the request.
	EvaluationID *string `locationName:"EvaluationId" type:"string"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file that contains logs of the CreateEvaluation operation.
	LogURI *string `locationName:"LogUri" type:"string"`

	// The ID of the MLModel that was the focus of the evaluation.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// A description of the most recent details about evaluating the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the Evaluation.
	Name *string `type:"string"`

	// Measurements of how well the MLModel performed using observations referenced
	// by the DataSource. One of the following metric is returned based on the type
	// of the MLModel:
	//
	//   BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique
	// to measure performance.
	//
	//   RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE)
	// technique to measure performance. RMSE measures the difference between predicted
	// and actual values for a single variable.
	//
	//   MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique
	// to measure performance.
	//
	//    For more information about performance metrics, please see the Amazon
	// Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	PerformanceMetrics *PerformanceMetrics `type:"structure"`

	// The status of the evaluation. This element can have one of the following
	// values:
	//
	//   PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate
	// an MLModel.  INPROGRESS - The evaluation is underway.  FAILED - The request
	// to evaluate an MLModel did not run to completion. It is not usable.  COMPLETED
	// - The evaluation process completed successfully.  DELETED - The Evaluation
	// is marked as deleted. It is not usable.
	Status *string `type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a GetEvaluation operation and describes an Evaluation.

type GetMLModelInput 

type GetMLModelInput struct {
	// The ID assigned to the MLModel at creation.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`

	// Specifies whether the GetMLModel operation should return Recipe.
	//
	// If true, Recipe is returned.
	//
	// If false, Recipe is not returned.
	Verbose *bool `type:"boolean"`
	// contains filtered or unexported fields
}

type GetMLModelOutput 

type GetMLModelOutput struct {
	// The time that the MLModel was created. The time is expressed in epoch time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The current endpoint of the MLModel
	EndpointInfo *RealtimeEndpointInfo `type:"structure"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the MLModel. The time is expressed in
	// epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file that contains logs of the CreateMLModel operation.
	LogURI *string `locationName:"LogUri" type:"string"`

	// The MLModel ID which is same as the MLModelId in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// Identifies the MLModel category. The following are the available types:
	//
	//  REGRESSION -- Produces a numeric result. For example, "What listing price
	// should a house have?" BINARY -- Produces one of two possible results. For
	// example, "Is this an e-commerce website?" MULTICLASS -- Produces more than
	// two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
	MLModelType *string `type:"string"`

	// Description of the most recent details about accessing the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the MLModel.
	Name *string `type:"string"`

	// The recipe to use when training the MLModel. The Recipe provides detailed
	// information about the observation data to use during training, as well as
	// manipulations to perform on the observation data during training.
	//
	// Note This parameter is provided as part of the verbose format.
	Recipe *string `type:"string"`

	// The schema used by all of the data files referenced by the DataSource.
	//
	// Note This parameter is provided as part of the verbose format.
	Schema *string `type:"string"`

	// The scoring threshold is used in binary classification MLModels, and marks
	// the boundary between a positive prediction and a negative prediction.
	//
	// Output values greater than or equal to the threshold receive a positive
	// result from the MLModel, such as true. Output values less than the threshold
	// receive a negative response from the MLModel, such as false.
	ScoreThreshold *float64 `type:"float"`

	// The time of the most recent edit to the ScoreThreshold. The time is expressed
	// in epoch time.
	ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// Long integer type that is a 64-bit signed number.
	SizeInBytes *int64 `type:"long"`

	// The current status of the MLModel. This element can have one of the following
	// values:
	//
	//   PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe
	// a MLModel.  INPROGRESS - The request is processing.  FAILED - The request
	// did not run to completion. It is not usable.  COMPLETED - The request completed
	// successfully.  DELETED - The MLModel is marked as deleted. It is not usable.
	Status *string `type:"string"`

	// The ID of the training DataSource.
	TrainingDataSourceID *string `locationName:"TrainingDataSourceId" type:"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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls
	// overfitting the data by penalizing large coefficients. This tends to drive
	// coefficients to zero, resulting in a sparse feature set. If you use this
	// parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
	//
	// The value is a double that ranges from 0 to MAX_DOUBLE. The default is not
	// to use L1 normalization. The parameter cannot be used when L2 is specified.
	// Use this parameter sparingly.
	//
	//   sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls
	// overfitting the data by penalizing large coefficients. This tends to drive
	// coefficients to small, nonzero values. If you use this parameter, specify
	// a small value, such as 1.0E-04 or 1.0E-08.
	//
	// The value is a double that ranges from 0 to MAX_DOUBLE. The default is not
	// to use L2 normalization. This parameter cannot be used when L1 is specified.
	// Use this parameter sparingly.
	//
	//  sgd.maxPasses - The number of times that the training process traverses
	// the observations to build the MLModel. The value is an integer that ranges
	// from 1 to 10000. The default value is 10.
	//
	//  sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending
	// on the input data, the model size might affect performance.
	//
	//  The value is an integer that ranges from 100000 to 2147483648. The default
	// value is 33554432.
	TrainingParameters map[string]*string `type:"map"`
	// contains filtered or unexported fields
}

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

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 *string `type:"string"`

	// The time that the MLModel was created. The time is expressed in epoch time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `locationName:"CreatedByIamUser" type:"string"`

	// The current endpoint of the MLModel.
	EndpointInfo *RealtimeEndpointInfo `type:"structure"`

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the MLModel. The time is expressed in
	// epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID assigned to the MLModel at creation.
	MLModelID *string `locationName:"MLModelId" type:"string"`

	// Identifies the MLModel category. The following are the available types:
	//
	//  REGRESSION - Produces a numeric result. For example, "What listing price
	// should a house have?". BINARY - Produces one of two possible results. For
	// example, "Is this a child-friendly web site?". MULTICLASS - Produces more
	// than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk
	// trade?".
	MLModelType *string `type:"string"`

	// A description of the most recent details about accessing the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the MLModel.
	Name *string `type:"string"`

	ScoreThreshold *float64 `type:"float"`

	// The time of the most recent edit to the ScoreThreshold. The time is expressed
	// in epoch time.
	ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// Long integer type that is a 64-bit signed number.
	SizeInBytes *int64 `type:"long"`

	// 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 did not run to completion. It is not usable. COMPLETED
	// - The creation process completed successfully. DELETED - The MLModel is marked
	// as deleted. It is not usable.
	Status *string `type:"string"`

	// The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
	TrainingDataSourceID *string `locationName:"TrainingDataSourceId" type:"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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls
	// overfitting the data by penalizing large coefficients. This tends to drive
	// coefficients to zero, resulting in a sparse feature set. If you use this
	// parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
	//
	// The value is a double that ranges from 0 to MAX_DOUBLE. The default is not
	// to use L1 normalization. The parameter cannot be used when L2 is specified.
	// Use this parameter sparingly.
	//
	//   sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls
	// overfitting the data by penalizing large coefficients. This tends to drive
	// coefficients to small, nonzero values. If you use this parameter, specify
	// a small value, such as 1.0E-04 or 1.0E-08.
	//
	// The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not
	// to use L2 normalization. This cannot be used when L1 is specified. Use this
	// parameter sparingly.
	//
	//  sgd.maxPasses - Number of times that the training process traverses the
	// observations to build the MLModel. The value is an integer that ranges from
	// 1 to 10000. The default value is 10.
	//
	//  sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending
	// on the input data, the model size might affect performance.
	//
	//  The value is an integer that ranges from 100000 to 2147483648. The default
	// value is 33554432.
	TrainingParameters map[string]*string `type:"map"`
	// contains filtered or unexported fields
}

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

type MachineLearning 

type MachineLearning struct {
	*aws.Service
}

MachineLearning is a client for Amazon Machine Learning.

func New 

func New(config *aws.Config) *MachineLearning

New returns a new MachineLearning client.

func (*MachineLearning) CreateBatchPrediction 

func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateBatchPredictionInput{
		BatchPredictionDataSourceID: aws.String("EntityId"), // Required
		BatchPredictionID:           aws.String("EntityId"), // Required
		MLModelID:                   aws.String("EntityId"), // Required
		OutputURI:                   aws.String("S3Url"),    // Required
		BatchPredictionName:         aws.String("EntityName"),
	}
	resp, err := svc.CreateBatchPrediction(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateBatchPredictionRequest 

func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *aws.Request, output *CreateBatchPredictionOutput)

CreateBatchPredictionRequest generates a request for the CreateBatchPrediction operation.

func (*MachineLearning) CreateDataSourceFromRDS 

func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)

Creates a DataSource object from an Amazon Relational Database Service (http://aws.amazon.com/rds/) (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. 

If Amazon ML cannot accept the input source, it sets the Status parameter

to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateDataSourceFromRDSInput{
		DataSourceID: aws.String("EntityId"), // Required
		RDSData: &machinelearning.RDSDataSpec{ // Required
			DatabaseCredentials: &machinelearning.RDSDatabaseCredentials{ // Required
				Password: aws.String("RDSDatabasePassword"), // Required
				Username: aws.String("RDSDatabaseUsername"), // Required
			},
			DatabaseInformation: &machinelearning.RDSDatabase{ // Required
				DatabaseName:       aws.String("RDSDatabaseName"),       // Required
				InstanceIdentifier: aws.String("RDSInstanceIdentifier"), // Required
			},
			ResourceRole:      aws.String("EDPResourceRole"), // Required
			S3StagingLocation: aws.String("S3Url"),           // Required
			SecurityGroupIDs: []*string{ // Required
				aws.String("EDPSecurityGroupId"), // Required
				// More values...
			},
			SelectSQLQuery:    aws.String("RDSSelectSqlQuery"), // Required
			ServiceRole:       aws.String("EDPServiceRole"),    // Required
			SubnetID:          aws.String("EDPSubnetId"),       // Required
			DataRearrangement: aws.String("DataRearrangement"),
			DataSchema:        aws.String("DataSchema"),
			DataSchemaURI:     aws.String("S3Url"),
		},
		RoleARN:           aws.String("RoleARN"), // Required
		ComputeStatistics: aws.Boolean(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromRDS(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateDataSourceFromRDSRequest 

func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *aws.Request, output *CreateDataSourceFromRDSOutput)

CreateDataSourceFromRDSRequest generates a request for the CreateDataSourceFromRDS operation.

func (*MachineLearning) CreateDataSourceFromRedshift 

func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)

Creates a DataSource from Amazon Redshift (http://aws.amazon.com/redshift/). A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. 

If Amazon ML cannot accept the input source, it sets the Status parameter

to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should exist in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery. Amazon ML executes Unload (http://docs.aws.amazon.com/redshift/latest/dg/t_Unloading_tables.html) command in Amazon Redshift to transfer the result set of SelectSqlQuery to S3StagingLocation.

After the DataSource is created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item -- a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateDataSourceFromRedshiftInput{
		DataSourceID: aws.String("EntityId"), // Required
		DataSpec: &machinelearning.RedshiftDataSpec{ // Required
			DatabaseCredentials: &machinelearning.RedshiftDatabaseCredentials{ // Required
				Password: aws.String("RedshiftDatabasePassword"), // Required
				Username: aws.String("RedshiftDatabaseUsername"), // Required
			},
			DatabaseInformation: &machinelearning.RedshiftDatabase{ // Required
				ClusterIdentifier: aws.String("RedshiftClusterIdentifier"), // Required
				DatabaseName:      aws.String("RedshiftDatabaseName"),      // Required
			},
			S3StagingLocation: aws.String("S3Url"),                  // Required
			SelectSQLQuery:    aws.String("RedshiftSelectSqlQuery"), // Required
			DataRearrangement: aws.String("DataRearrangement"),
			DataSchema:        aws.String("DataSchema"),
			DataSchemaURI:     aws.String("S3Url"),
		},
		RoleARN:           aws.String("RoleARN"), // Required
		ComputeStatistics: aws.Boolean(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromRedshift(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateDataSourceFromRedshiftRequest 

func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *aws.Request, output *CreateDataSourceFromRedshiftOutput)

CreateDataSourceFromRedshiftRequest generates a request for the CreateDataSourceFromRedshift operation.

func (*MachineLearning) CreateDataSourceFromS3 

func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction operations. 

If Amazon ML cannot accept the input source, it sets the Status parameter

to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item: a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateDataSourceFromS3Input{
		DataSourceID: aws.String("EntityId"), // Required
		DataSpec: &machinelearning.S3DataSpec{ // Required
			DataLocationS3:       aws.String("S3Url"), // Required
			DataRearrangement:    aws.String("DataRearrangement"),
			DataSchema:           aws.String("DataSchema"),
			DataSchemaLocationS3: aws.String("S3Url"),
		},
		ComputeStatistics: aws.Boolean(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromS3(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateDataSourceFromS3Request 

func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *aws.Request, output *CreateDataSourceFromS3Output)

CreateDataSourceFromS3Request generates a request for the CreateDataSourceFromS3 operation.

func (*MachineLearning) CreateEvaluation 

func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateEvaluationInput{
		EvaluationDataSourceID: aws.String("EntityId"), // Required
		EvaluationID:           aws.String("EntityId"), // Required
		MLModelID:              aws.String("EntityId"), // Required
		EvaluationName:         aws.String("EntityName"),
	}
	resp, err := svc.CreateEvaluation(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateEvaluationRequest 

func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *aws.Request, output *CreateEvaluationOutput)

CreateEvaluationRequest generates a request for the CreateEvaluation operation.

func (*MachineLearning) CreateMLModel 

func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)

Creates a new MLModel using the data files and the recipe as information sources.

An MLModel is nearly immutable. Users can only update the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check progress of the MLModel during the creation operation. 

CreateMLModel requires a DataSource with computed statistics, which can

be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateMLModelInput{
		MLModelID:            aws.String("EntityId"),    // Required
		MLModelType:          aws.String("MLModelType"), // Required
		TrainingDataSourceID: aws.String("EntityId"),    // Required
		MLModelName:          aws.String("EntityName"),
		Parameters: map[string]*string{
			"Key": aws.String("StringType"), // Required
			// More values...
		},
		Recipe:    aws.String("Recipe"),
		RecipeURI: aws.String("S3Url"),
	}
	resp, err := svc.CreateMLModel(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateMLModelRequest 

func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *aws.Request, output *CreateMLModelOutput)

CreateMLModelRequest generates a request for the CreateMLModel operation.

func (*MachineLearning) CreateRealtimeEndpoint 

func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.CreateRealtimeEndpointInput{
		MLModelID: aws.String("EntityId"), // Required
	}
	resp, err := svc.CreateRealtimeEndpoint(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) CreateRealtimeEndpointRequest 

func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *aws.Request, output *CreateRealtimeEndpointOutput)

CreateRealtimeEndpointRequest generates a request for the CreateRealtimeEndpoint operation.

func (*MachineLearning) DeleteBatchPrediction 

func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution The result of the DeleteBatchPrediction operation is irreversible.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DeleteBatchPredictionInput{
		BatchPredictionID: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteBatchPrediction(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DeleteBatchPredictionRequest 

func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *aws.Request, output *DeleteBatchPredictionOutput)

DeleteBatchPredictionRequest generates a request for the DeleteBatchPrediction operation.

func (*MachineLearning) DeleteDataSource 

func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution The results of the DeleteDataSource operation are irreversible.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DeleteDataSourceInput{
		DataSourceID: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteDataSource(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DeleteDataSourceRequest 

func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *aws.Request, output *DeleteDataSourceOutput)

DeleteDataSourceRequest generates a request for the DeleteDataSource operation.

func (*MachineLearning) DeleteEvaluation 

func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

Caution The results of the DeleteEvaluation operation are irreversible.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DeleteEvaluationInput{
		EvaluationID: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteEvaluation(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DeleteEvaluationRequest 

func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *aws.Request, output *DeleteEvaluationOutput)

DeleteEvaluationRequest generates a request for the DeleteEvaluation operation.

func (*MachineLearning) DeleteMLModel 

func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution The result of the DeleteMLModel operation is irreversible.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DeleteMLModelInput{
		MLModelID: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteMLModel(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DeleteMLModelRequest 

func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *aws.Request, output *DeleteMLModelOutput)

DeleteMLModelRequest generates a request for the DeleteMLModel operation.

func (*MachineLearning) DeleteRealtimeEndpoint 

func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)

Deletes a real time endpoint of an MLModel.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DeleteRealtimeEndpointInput{
		MLModelID: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteRealtimeEndpoint(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DeleteRealtimeEndpointRequest 

func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *aws.Request, output *DeleteRealtimeEndpointOutput)

DeleteRealtimeEndpointRequest generates a request for the DeleteRealtimeEndpoint operation.

func (*MachineLearning) DescribeBatchPredictions 

func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)

Returns a list of BatchPrediction operations that match the search criteria in the request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DescribeBatchPredictionsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("BatchPredictionFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Long(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeBatchPredictions(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DescribeBatchPredictionsPages 

func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, fn func(p *DescribeBatchPredictionsOutput, lastPage bool) (shouldContinue bool)) error

func (*MachineLearning) DescribeBatchPredictionsRequest 

func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *aws.Request, output *DescribeBatchPredictionsOutput)

DescribeBatchPredictionsRequest generates a request for the DescribeBatchPredictions operation.

func (*MachineLearning) DescribeDataSources 

func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)

Returns a list of DataSource that match the search criteria in the request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DescribeDataSourcesInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("DataSourceFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Long(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeDataSources(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DescribeDataSourcesPages 

func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, fn func(p *DescribeDataSourcesOutput, lastPage bool) (shouldContinue bool)) error

func (*MachineLearning) DescribeDataSourcesRequest 

func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *aws.Request, output *DescribeDataSourcesOutput)

DescribeDataSourcesRequest generates a request for the DescribeDataSources operation.

func (*MachineLearning) DescribeEvaluations 

func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)

Returns a list of DescribeEvaluations that match the search criteria in the request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DescribeEvaluationsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("EvaluationFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Long(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeEvaluations(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DescribeEvaluationsPages 

func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, fn func(p *DescribeEvaluationsOutput, lastPage bool) (shouldContinue bool)) error

func (*MachineLearning) DescribeEvaluationsRequest 

func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *aws.Request, output *DescribeEvaluationsOutput)

DescribeEvaluationsRequest generates a request for the DescribeEvaluations operation.

func (*MachineLearning) DescribeMLModels 

func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)

Returns a list of MLModel that match the search criteria in the request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.DescribeMLModelsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("MLModelFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Long(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeMLModels(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) DescribeMLModelsPages 

func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(p *DescribeMLModelsOutput, lastPage bool) (shouldContinue bool)) error

func (*MachineLearning) DescribeMLModelsRequest 

func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *aws.Request, output *DescribeMLModelsOutput)

DescribeMLModelsRequest generates a request for the DescribeMLModels operation.

func (*MachineLearning) GetBatchPrediction 

func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.GetBatchPredictionInput{
		BatchPredictionID: aws.String("EntityId"), // Required
	}
	resp, err := svc.GetBatchPrediction(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) GetBatchPredictionRequest 

func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *aws.Request, output *GetBatchPredictionOutput)

GetBatchPredictionRequest generates a request for the GetBatchPrediction operation.

func (*MachineLearning) GetDataSource 

func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.GetDataSourceInput{
		DataSourceID: aws.String("EntityId"), // Required
		Verbose:      aws.Boolean(true),
	}
	resp, err := svc.GetDataSource(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) GetDataSourceRequest 

func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *aws.Request, output *GetDataSourceOutput)

GetDataSourceRequest generates a request for the GetDataSource operation.

func (*MachineLearning) GetEvaluation 

func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.GetEvaluationInput{
		EvaluationID: aws.String("EntityId"), // Required
	}
	resp, err := svc.GetEvaluation(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) GetEvaluationRequest 

func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *aws.Request, output *GetEvaluationOutput)

GetEvaluationRequest generates a request for the GetEvaluation operation.

func (*MachineLearning) GetMLModel 

func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)

Returns an MLModel that includes detailed metadata, and data source information as well as the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.GetMLModelInput{
		MLModelID: aws.String("EntityId"), // Required
		Verbose:   aws.Boolean(true),
	}
	resp, err := svc.GetMLModel(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) GetMLModelRequest 

func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *aws.Request, output *GetMLModelOutput)

GetMLModelRequest generates a request for the GetMLModel operation.

func (*MachineLearning) Predict 

func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)

Generates a prediction for the observation using the specified MLModel.

Note Not all response parameters will be populated because this is dependent on the type of requested model.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.PredictInput{
		MLModelID:       aws.String("EntityId"), // Required
		PredictEndpoint: aws.String("VipURL"),   // Required
		Record: map[string]*string{ // Required
			"Key": aws.String("VariableValue"), // Required
			// More values...
		},
	}
	resp, err := svc.Predict(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) PredictRequest 

func (c *MachineLearning) PredictRequest(input *PredictInput) (req *aws.Request, output *PredictOutput)

PredictRequest generates a request for the Predict operation.

func (*MachineLearning) UpdateBatchPrediction 

func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.UpdateBatchPredictionInput{
		BatchPredictionID:   aws.String("EntityId"),   // Required
		BatchPredictionName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateBatchPrediction(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) UpdateBatchPredictionRequest 

func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *aws.Request, output *UpdateBatchPredictionOutput)

UpdateBatchPredictionRequest generates a request for the UpdateBatchPrediction operation.

func (*MachineLearning) UpdateDataSource 

func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.UpdateDataSourceInput{
		DataSourceID:   aws.String("EntityId"),   // Required
		DataSourceName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateDataSource(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) UpdateDataSourceRequest 

func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *aws.Request, output *UpdateDataSourceOutput)

UpdateDataSourceRequest generates a request for the UpdateDataSource operation.

func (*MachineLearning) UpdateEvaluation 

func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.UpdateEvaluationInput{
		EvaluationID:   aws.String("EntityId"),   // Required
		EvaluationName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateEvaluation(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) UpdateEvaluationRequest 

func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *aws.Request, output *UpdateEvaluationOutput)

UpdateEvaluationRequest generates a request for the UpdateEvaluation operation.

func (*MachineLearning) UpdateMLModel 

func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/awserr"
	"github.com/aws/aws-sdk-go/aws/awsutil"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	svc := machinelearning.New(nil)

	params := &machinelearning.UpdateMLModelInput{
		MLModelID:      aws.String("EntityId"), // Required
		MLModelName:    aws.String("EntityName"),
		ScoreThreshold: aws.Double(1.0),
	}
	resp, err := svc.UpdateMLModel(params)

	if err != nil {
		if awsErr, ok := err.(awserr.Error); ok {
			// Generic AWS Error with Code, Message, and original error (if any)
			fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
			if reqErr, ok := err.(awserr.RequestFailure); ok {
				// A service error occurred
				fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
			}
		} else {
			// This case should never be hit, the SDK should always return an
			// error which satisfies the awserr.Error interface.
			fmt.Println(err.Error())
		}
	}

	// Pretty-print the response data.
	fmt.Println(awsutil.StringValue(resp))
}

Output:

func (*MachineLearning) UpdateMLModelRequest 

func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *aws.Request, output *UpdateMLModelOutput)

UpdateMLModelRequest generates a request for the UpdateMLModel operation.

type PerformanceMetrics 

type PerformanceMetrics struct {
	Properties map[string]*string `type:"map"`
	// contains filtered or unexported fields
}

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 (http://docs.aws.amazon.com/machine-learning/latest/dg).

type PredictInput 

type PredictInput struct {
	// A unique identifier of the MLModel.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`

	PredictEndpoint *string `type:"string" required:"true"`

	// A map of variable name-value pairs that represent an observation.
	Record map[string]*string `type:"map" required:"true"`
	// contains filtered or unexported fields
}

type PredictOutput 

type PredictOutput struct {
	// The output from a Predict operation:
	//
	//    Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
	// - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
	//
	//    PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
	//
	//    PredictedScores - Contains the raw classification score corresponding
	// to each label.
	//
	//    PredictedValue - Present for a REGRESSION MLModel request.
	Prediction *Prediction `type:"structure"`
	// contains filtered or unexported fields
}

type Prediction 

type Prediction struct {
	// Provides any additional details regarding the prediction.
	Details map[string]*string `locationName:"details" type:"map"`

	// The prediction label for either a BINARY or MULTICLASS MLModel.
	PredictedLabel *string `locationName:"predictedLabel" type:"string"`

	// Provides the raw classification score corresponding to each label.
	PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"`

	// The prediction value for REGRESSION MLModel.
	PredictedValue *float64 `locationName:"predictedValue" type:"float"`
	// contains filtered or unexported fields
}

The output from a Predict operation: 

Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE

- REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD 

PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.

PredictedScores - Contains the raw classification score corresponding

to each label. 

PredictedValue - Present for a REGRESSION MLModel request.

type RDSDataSpec 

type RDSDataSpec struct {
	// DataRearrangement - A JSON string that represents the splitting requirement
	// of a DataSource.
	//
	//   Sample -  "{\"randomSeed\":\"some-random-seed\", \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema. This is not required if DataSchemaUri
	// is specified.
	DataSchema *string `type:"string"`

	// The Amazon S3 location of the DataSchema.
	DataSchemaURI *string `locationName:"DataSchemaUri" type:"string"`

	// The AWS Identity and Access Management (IAM) credentials that are used connect
	// to the Amazon RDS database.
	DatabaseCredentials *RDSDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
	DatabaseInformation *RDSDatabase `type:"structure" required:"true"`

	// 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 (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ResourceRole *string `type:"string" required:"true"`

	// The Amazon S3 location for staging Amazon RDS data. The data retrieved from
	// Amazon RDS using SelectSqlQuery is stored in this location.
	S3StagingLocation *string `type:"string" required:"true"`

	// 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.
	SecurityGroupIDs []*string `locationName:"SecurityGroupIds" type:"list" required:"true"`

	// The query that is used to retrieve the observation data for the DataSource.
	SelectSQLQuery *string `locationName:"SelectSqlQuery" type:"string" required:"true"`

	// 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 (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ServiceRole *string `type:"string" required:"true"`

	// 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.
	SubnetID *string `locationName:"SubnetId" type:"string" required:"true"`
	// contains filtered or unexported fields
}

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.
	DatabaseName *string `type:"string" required:"true"`

	// The ID of an RDS DB instance.
	InstanceIdentifier *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

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.
	Password *string `type:"string" required:"true"`

	// 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.
	Username *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

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 `locationName:"DataPipelineId" type:"string"`

	// The database details required to connect to an Amazon RDS.
	Database *RDSDatabase `type:"structure"`

	// 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 `type:"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 (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ResourceRole *string `type:"string"`

	// The SQL query that is supplied during CreateDataSourceFromRDS. Returns only
	// if Verbose is true in GetDataSourceInput.
	SelectSQLQuery *string `locationName:"SelectSqlQuery" type:"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 (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ServiceRole *string `type:"string"`
	// contains filtered or unexported fields
}

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 `type:"timestamp" timestampFormat:"unix"`

	// 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 *string `type:"string"`

	// 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 `locationName:"EndpointUrl" type:"string"`

	// The maximum processing rate for the real-time endpoint for MLModel, measured
	// in incoming requests per second.
	PeakRequestsPerSecond *int64 `type:"integer"`
	// contains filtered or unexported fields
}

Describes the real-time endpoint information for an MLModel.

type RedshiftDataSpec 

type RedshiftDataSpec struct {
	// Describes the splitting specifications for a DataSource.
	DataRearrangement *string `type:"string"`

	// Describes the schema for an Amazon Redshift DataSource.
	DataSchema *string `type:"string"`

	// Describes the schema location for an Amazon Redshift DataSource.
	DataSchemaURI *string `locationName:"DataSchemaUri" type:"string"`

	// Describes AWS Identity and Access Management (IAM) credentials that are used
	// connect to the Amazon Redshift database.
	DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
	DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"`

	// Describes an Amazon S3 location to store the result set of the SelectSqlQuery
	// query.
	S3StagingLocation *string `type:"string" required:"true"`

	// Describes the SQL Query to execute on an Amazon Redshift database for an
	// Amazon Redshift DataSource.
	SelectSQLQuery *string `locationName:"SelectSqlQuery" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the data specification of an Amazon Redshift DataSource.

type RedshiftDatabase 

type RedshiftDatabase struct {
	// The ID of an Amazon Redshift cluster.
	ClusterIdentifier *string `type:"string" required:"true"`

	// The name of a database hosted on an Amazon Redshift cluster.
	DatabaseName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

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 (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	Password *string `type:"string" required:"true"`

	// 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 (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	Username *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

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 (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	DatabaseUserName *string `type:"string"`

	// Describes the database details required to connect to an Amazon Redshift
	// database.
	RedshiftDatabase *RedshiftDatabase `type:"structure"`

	// The SQL query that is specified during CreateDataSourceFromRedshift. Returns
	// only if Verbose is true in GetDataSourceInput.
	SelectSQLQuery *string `locationName:"SelectSqlQuery" type:"string"`
	// contains filtered or unexported fields
}

Describes the DataSource details specific to Amazon Redshift.

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.
	DataLocationS3 *string `type:"string" required:"true"`

	// Describes the splitting requirement of a Datasource.
	DataRearrangement *string `type:"string"`

	// Describes the schema for an Amazon S3 DataSource.
	DataSchema *string `type:"string"`

	// Describes the schema Location in Amazon S3.
	DataSchemaLocationS3 *string `type:"string"`
	// contains filtered or unexported fields
}

Describes the data specification of a DataSource.

type UpdateBatchPredictionInput 

type UpdateBatchPredictionInput struct {
	// The ID assigned to the BatchPrediction during creation.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string" required:"true"`

	// A new user-supplied name or description of the BatchPrediction.
	BatchPredictionName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

type UpdateBatchPredictionOutput 

type UpdateBatchPredictionOutput struct {
	// The ID assigned to the BatchPrediction during creation. This value should
	// be identical to the value of the BatchPredictionId in the request.
	BatchPredictionID *string `locationName:"BatchPredictionId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

type UpdateDataSourceInput 

type UpdateDataSourceInput struct {
	// The ID assigned to the DataSource during creation.
	DataSourceID *string `locationName:"DataSourceId" type:"string" required:"true"`

	// A new user-supplied name or description of the DataSource that will replace
	// the current description.
	DataSourceName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

type UpdateDataSourceOutput 

type UpdateDataSourceOutput struct {
	// The ID assigned to the DataSource during creation. This value should be identical
	// to the value of the DataSourceID in the request.
	DataSourceID *string `locationName:"DataSourceId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

type UpdateEvaluationInput 

type UpdateEvaluationInput struct {
	// The ID assigned to the Evaluation during creation.
	EvaluationID *string `locationName:"EvaluationId" type:"string" required:"true"`

	// A new user-supplied name or description of the Evaluation that will replace
	// the current content.
	EvaluationName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

type UpdateEvaluationOutput 

type UpdateEvaluationOutput struct {
	// The ID assigned to the Evaluation during creation. This value should be identical
	// to the value of the Evaluation in the request.
	EvaluationID *string `locationName:"EvaluationId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

type UpdateMLModelInput 

type UpdateMLModelInput struct {
	// The ID assigned to the MLModel during creation.
	MLModelID *string `locationName:"MLModelId" type:"string" required:"true"`

	// A user-supplied name or description of the MLModel.
	MLModelName *string `type:"string"`

	// The ScoreThreshold used in binary classification MLModel that marks the boundary
	// between a positive prediction and a negative prediction.
	//
	// Output values greater than or equal to the ScoreThreshold receive a positive
	// result from the MLModel, such as true. Output values less than the ScoreThreshold
	// receive a negative response from the MLModel, such as false.
	ScoreThreshold *float64 `type:"float"`
	// contains filtered or unexported fields
}

type UpdateMLModelOutput 

type UpdateMLModelOutput struct {
	// The ID assigned to the MLModel during creation. This value should be identical
	// to the value of the MLModelID in the request.
	MLModelID *string `locationName:"MLModelId" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.

Source Files

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Path Synopsis
Package machinelearningiface provides an interface for the Amazon Machine Learning.
 Package machinelearningiface provides an interface for the Amazon Machine Learning.

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