// Code generated by private/model/cli/gen-api/main.go. DO NOT EDIT.

package machinelearning

import (
	"time"

	"github.com/aws/aws-sdk-go-v2/aws"
	"github.com/aws/aws-sdk-go-v2/internal/awsutil"
)

var _ aws.Config
var _ = awsutil.Prettify

// Represents the output of a GetBatchPrediction operation.
//
// The content consists of the detailed metadata, the status, and the data file
// information of a Batch Prediction.
type BatchPrediction struct {
	_ struct{} `type:"structure"`

	// The ID of the DataSource that points to the group of observations to predict.
	BatchPredictionDataSourceId *string `min:"1" 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 `min:"1" type:"string"`

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

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

	// 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 `type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp"`

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

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

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

	// The ID of the MLModel that generated predictions for the BatchPrediction
	// request.
	MLModelId *string `min:"1" 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 `type:"string"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp"`

	// The status of the BatchPrediction. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    generate predictions for a batch of observations.
	//
	//    * INPROGRESS - The process is underway.
	//
	//    * FAILED - The request to perform a batch prediction did not run to completion.
	//    It is not usable.
	//
	//    * COMPLETED - The batch prediction process completed successfully.
	//
	//    * DELETED - The BatchPrediction is marked as deleted. It is not usable.
	Status EntityStatus `type:"string" enum:"true"`

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

// String returns the string representation
func (s BatchPrediction) String() string {
	return awsutil.Prettify(s)
}

// 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 DataSource struct {
	_ struct{} `type:"structure"`

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

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

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

	// 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 `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 and rearrangement requirement
	// used when this DataSource was created.
	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 `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp"`

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

	// 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 `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp"`

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

// String returns the string representation
func (s DataSource) String() string {
	return awsutil.Prettify(s)
}

// 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 Evaluation struct {
	_ struct{} `type:"structure"`

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

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

	// 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 `type:"string"`

	// The ID of the DataSource that is used to evaluate the MLModel.
	EvaluationDataSourceId *string `min:"1" type:"string"`

	// The ID that is assigned to the Evaluation at creation.
	EvaluationId *string `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp"`

	// 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"`

	// The ID of the MLModel that is the focus of the evaluation.
	MLModelId *string `min:"1" 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"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp"`

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

// String returns the string representation
func (s Evaluation) String() string {
	return awsutil.Prettify(s)
}

// Represents the output of a GetMLModel operation.
//
// The content consists of the detailed metadata and the current status of the
// MLModel.
type MLModel struct {
	_ struct{} `type:"structure"`

	// The algorithm used to train the MLModel. The following algorithm is supported:
	//
	//    * SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
	//    gradient of the loss function.
	Algorithm Algorithm `type:"string" enum:"true"`

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

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

	// 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 `type:"string"`

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

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp"`

	// 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"`

	// The ID assigned to the MLModel at creation.
	MLModelId *string `min:"1" type:"string"`

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

	// 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"`

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

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp"`

	// The current status of an MLModel. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    create an MLModel.
	//
	//    * INPROGRESS - The creation process is underway.
	//
	//    * FAILED - The request to create an MLModel didn't run to completion.
	//    The model isn't usable.
	//
	//    * COMPLETED - The creation process completed successfully.
	//
	//    * DELETED - The MLModel is marked as deleted. It isn't usable.
	Status EntityStatus `type:"string" enum:"true"`

	// The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
	TrainingDataSourceId *string `min:"1" 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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending
	//    on the input data, the size of the model might affect its performance.
	//    The value is an integer that ranges from 100000 to 2147483648. The default
	//    value is 33554432.
	//
	//    * sgd.maxPasses - The number of times that the training process traverses
	//    the observations to build the MLModel. The value is an integer that ranges
	//    from 1 to 10000. The default value is 10.
	//
	//    * sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling
	//    the data improves a model's ability to find the optimal solution for a
	//    variety of data types. The valid values are auto and none. The default
	//    value is none.
	//
	//    * sgd.l1RegularizationAmount - The coefficient regularization L1 norm,
	//    which controls overfitting the data by penalizing large coefficients.
	//    This parameter tends to drive coefficients to zero, resulting in sparse
	//    feature set. If you use this parameter, start by specifying a small value,
	//    such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE.
	//    The default is to not use L1 normalization. This parameter can't be used
	//    when L2 is specified. Use this parameter sparingly.
	//
	//    * sgd.l2RegularizationAmount - The coefficient regularization L2 norm,
	//    which controls overfitting the data by penalizing large coefficients.
	//    This tends to drive coefficients to small, nonzero values. If you use
	//    this parameter, start by specifying a small value, such as 1.0E-08. The
	//    value is a double that ranges from 0 to MAX_DOUBLE. The default is to
	//    not use L2 normalization. This parameter can't be used when L1 is specified.
	//    Use this parameter sparingly.
	TrainingParameters map[string]string `type:"map"`
}

// String returns the string representation
func (s MLModel) String() string {
	return awsutil.Prettify(s)
}

// 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 PerformanceMetrics struct {
	_ struct{} `type:"structure"`

	Properties map[string]string `type:"map"`
}

// String returns the string representation
func (s PerformanceMetrics) String() string {
	return awsutil.Prettify(s)
}

// 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 Prediction struct {
	_ struct{} `type:"structure"`

	// 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" min:"1" 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"`
}

// String returns the string representation
func (s Prediction) String() string {
	return awsutil.Prettify(s)
}

// The data specification of an Amazon Relational Database Service (Amazon RDS)
// DataSource.
type RDSDataSpec struct {
	_ struct{} `type:"structure"`

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon RDS DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

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

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

	// Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
	//
	// DatabaseInformation is a required field
	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 is a required field
	ResourceRole *string `min:"1" 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 is a required field
	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 is a required field
	SecurityGroupIds []string `type:"list" required:"true"`

	// The query that is used to retrieve the observation data for the DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" 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 is a required field
	ServiceRole *string `min:"1" 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 is a required field
	SubnetId *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RDSDataSpec) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RDSDataSpec) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RDSDataSpec"}

	if s.DatabaseCredentials == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseCredentials"))
	}

	if s.DatabaseInformation == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseInformation"))
	}

	if s.ResourceRole == nil {
		invalidParams.Add(aws.NewErrParamRequired("ResourceRole"))
	}
	if s.ResourceRole != nil && len(*s.ResourceRole) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("ResourceRole", 1))
	}

	if s.S3StagingLocation == nil {
		invalidParams.Add(aws.NewErrParamRequired("S3StagingLocation"))
	}

	if s.SecurityGroupIds == nil {
		invalidParams.Add(aws.NewErrParamRequired("SecurityGroupIds"))
	}

	if s.SelectSqlQuery == nil {
		invalidParams.Add(aws.NewErrParamRequired("SelectSqlQuery"))
	}
	if s.SelectSqlQuery != nil && len(*s.SelectSqlQuery) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("SelectSqlQuery", 1))
	}

	if s.ServiceRole == nil {
		invalidParams.Add(aws.NewErrParamRequired("ServiceRole"))
	}
	if s.ServiceRole != nil && len(*s.ServiceRole) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("ServiceRole", 1))
	}

	if s.SubnetId == nil {
		invalidParams.Add(aws.NewErrParamRequired("SubnetId"))
	}
	if s.SubnetId != nil && len(*s.SubnetId) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("SubnetId", 1))
	}
	if s.DatabaseCredentials != nil {
		if err := s.DatabaseCredentials.Validate(); err != nil {
			invalidParams.AddNested("DatabaseCredentials", err.(aws.ErrInvalidParams))
		}
	}
	if s.DatabaseInformation != nil {
		if err := s.DatabaseInformation.Validate(); err != nil {
			invalidParams.AddNested("DatabaseInformation", err.(aws.ErrInvalidParams))
		}
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// The database details of an Amazon RDS database.
type RDSDatabase struct {
	_ struct{} `type:"structure"`

	// The name of a database hosted on an RDS DB instance.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`

	// The ID of an RDS DB instance.
	//
	// InstanceIdentifier is a required field
	InstanceIdentifier *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RDSDatabase) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RDSDatabase) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RDSDatabase"}

	if s.DatabaseName == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseName"))
	}
	if s.DatabaseName != nil && len(*s.DatabaseName) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("DatabaseName", 1))
	}

	if s.InstanceIdentifier == nil {
		invalidParams.Add(aws.NewErrParamRequired("InstanceIdentifier"))
	}
	if s.InstanceIdentifier != nil && len(*s.InstanceIdentifier) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("InstanceIdentifier", 1))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// The database credentials to connect to a database on an RDS DB instance.
type RDSDatabaseCredentials struct {
	_ struct{} `type:"structure"`

	// 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 is a required field
	Password *string `min:"8" 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 is a required field
	Username *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RDSDatabaseCredentials) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RDSDatabaseCredentials) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RDSDatabaseCredentials"}

	if s.Password == nil {
		invalidParams.Add(aws.NewErrParamRequired("Password"))
	}
	if s.Password != nil && len(*s.Password) < 8 {
		invalidParams.Add(aws.NewErrParamMinLen("Password", 8))
	}

	if s.Username == nil {
		invalidParams.Add(aws.NewErrParamRequired("Username"))
	}
	if s.Username != nil && len(*s.Username) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("Username", 1))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// The datasource details that are specific to Amazon RDS.
type RDSMetadata struct {
	_ struct{} `type:"structure"`

	// 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 `min:"1" 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 `min:"1" 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 `min:"1" type:"string"`

	// The SQL query that is supplied during CreateDataSourceFromRDS. Returns only
	// if Verbose is true in GetDataSourceInput.
	SelectSqlQuery *string `min:"1" 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 `min:"1" type:"string"`
}

// String returns the string representation
func (s RDSMetadata) String() string {
	return awsutil.Prettify(s)
}

// Describes the real-time endpoint information for an MLModel.
type RealtimeEndpointInfo struct {
	_ struct{} `type:"structure"`

	// 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"`

	// The current status of the real-time endpoint for the MLModel. This element
	// can have one of the following values:
	//
	//    * NONE - Endpoint does not exist or was previously deleted.
	//
	//    * READY - Endpoint is ready to be used for real-time predictions.
	//
	//    * UPDATING - Updating/creating the endpoint.
	EndpointStatus RealtimeEndpointStatus `type:"string" enum:"true"`

	// 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 `type:"string"`

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

// String returns the string representation
func (s RealtimeEndpointInfo) String() string {
	return awsutil.Prettify(s)
}

// Describes the data specification of an Amazon Redshift DataSource.
type RedshiftDataSpec struct {
	_ struct{} `type:"structure"`

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon Redshift DataSource.
	// The DataSchema defines the structure of the observation data in the data
	// file(s) referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

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

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

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

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

	// Describes the SQL Query to execute on an Amazon Redshift database for an
	// Amazon Redshift DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RedshiftDataSpec) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RedshiftDataSpec) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RedshiftDataSpec"}

	if s.DatabaseCredentials == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseCredentials"))
	}

	if s.DatabaseInformation == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseInformation"))
	}

	if s.S3StagingLocation == nil {
		invalidParams.Add(aws.NewErrParamRequired("S3StagingLocation"))
	}

	if s.SelectSqlQuery == nil {
		invalidParams.Add(aws.NewErrParamRequired("SelectSqlQuery"))
	}
	if s.SelectSqlQuery != nil && len(*s.SelectSqlQuery) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("SelectSqlQuery", 1))
	}
	if s.DatabaseCredentials != nil {
		if err := s.DatabaseCredentials.Validate(); err != nil {
			invalidParams.AddNested("DatabaseCredentials", err.(aws.ErrInvalidParams))
		}
	}
	if s.DatabaseInformation != nil {
		if err := s.DatabaseInformation.Validate(); err != nil {
			invalidParams.AddNested("DatabaseInformation", err.(aws.ErrInvalidParams))
		}
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// Describes the database details required to connect to an Amazon Redshift
// database.
type RedshiftDatabase struct {
	_ struct{} `type:"structure"`

	// The ID of an Amazon Redshift cluster.
	//
	// ClusterIdentifier is a required field
	ClusterIdentifier *string `min:"1" type:"string" required:"true"`

	// The name of a database hosted on an Amazon Redshift cluster.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RedshiftDatabase) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RedshiftDatabase) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RedshiftDatabase"}

	if s.ClusterIdentifier == nil {
		invalidParams.Add(aws.NewErrParamRequired("ClusterIdentifier"))
	}
	if s.ClusterIdentifier != nil && len(*s.ClusterIdentifier) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("ClusterIdentifier", 1))
	}

	if s.DatabaseName == nil {
		invalidParams.Add(aws.NewErrParamRequired("DatabaseName"))
	}
	if s.DatabaseName != nil && len(*s.DatabaseName) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("DatabaseName", 1))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// Describes the database credentials for connecting to a database on an Amazon
// Redshift cluster.
type RedshiftDatabaseCredentials struct {
	_ struct{} `type:"structure"`

	// 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 is a required field
	Password *string `min:"8" 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 is a required field
	Username *string `min:"1" type:"string" required:"true"`
}

// String returns the string representation
func (s RedshiftDatabaseCredentials) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *RedshiftDatabaseCredentials) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "RedshiftDatabaseCredentials"}

	if s.Password == nil {
		invalidParams.Add(aws.NewErrParamRequired("Password"))
	}
	if s.Password != nil && len(*s.Password) < 8 {
		invalidParams.Add(aws.NewErrParamMinLen("Password", 8))
	}

	if s.Username == nil {
		invalidParams.Add(aws.NewErrParamRequired("Username"))
	}
	if s.Username != nil && len(*s.Username) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("Username", 1))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// Describes the DataSource details specific to Amazon Redshift.
type RedshiftMetadata struct {
	_ struct{} `type:"structure"`

	// 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 `min:"1" 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 `min:"1" type:"string"`
}

// String returns the string representation
func (s RedshiftMetadata) String() string {
	return awsutil.Prettify(s)
}

// Describes the data specification of a DataSource.
type S3DataSpec struct {
	_ struct{} `type:"structure"`

	// 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 is a required field
	DataLocationS3 *string `type:"string" required:"true"`

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon S3 DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// You must provide either the DataSchema or the DataSchemaLocationS3.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// Describes the schema location in Amazon S3. You must provide either the DataSchema
	// or the DataSchemaLocationS3.
	DataSchemaLocationS3 *string `type:"string"`
}

// String returns the string representation
func (s S3DataSpec) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *S3DataSpec) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "S3DataSpec"}

	if s.DataLocationS3 == nil {
		invalidParams.Add(aws.NewErrParamRequired("DataLocationS3"))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// A custom key-value pair associated with an ML object, such as an ML model.
type Tag struct {
	_ struct{} `type:"structure"`

	// A unique identifier for the tag. Valid characters include Unicode letters,
	// digits, white space, _, ., /, =, +, -, %, and @.
	Key *string `min:"1" type:"string"`

	// An optional string, typically used to describe or define the tag. Valid characters
	// include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
	Value *string `type:"string"`
}

// String returns the string representation
func (s Tag) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *Tag) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "Tag"}
	if s.Key != nil && len(*s.Key) < 1 {
		invalidParams.Add(aws.NewErrParamMinLen("Key", 1))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}