R/machinelearning_interfaces.R

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common populate
#' @include machinelearning_service.R
NULL

.machinelearning$add_tags_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$add_tags_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_batch_prediction_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string")), BatchPredictionName = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), BatchPredictionDataSourceId = structure(logical(0), tags = list(type = "string")), OutputUri = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_batch_prediction_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_rds_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataSourceName = structure(logical(0), tags = list(type = "string")), RDSData = structure(list(DatabaseInformation = structure(list(InstanceIdentifier = structure(logical(0), tags = list(type = "string")), DatabaseName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SelectSqlQuery = structure(logical(0), tags = list(type = "string")), DatabaseCredentials = structure(list(Username = structure(logical(0), tags = list(type = "string")), Password = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), S3StagingLocation = structure(logical(0), tags = list(type = "string")), DataRearrangement = structure(logical(0), tags = list(type = "string")), DataSchema = structure(logical(0), tags = list(type = "string")), DataSchemaUri = structure(logical(0), tags = list(type = "string")), ResourceRole = structure(logical(0), tags = list(type = "string")), ServiceRole = structure(logical(0), tags = list(type = "string")), SubnetId = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), RoleARN = structure(logical(0), tags = list(type = "string")), ComputeStatistics = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_rds_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_redshift_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataSourceName = structure(logical(0), tags = list(type = "string")), DataSpec = structure(list(DatabaseInformation = structure(list(DatabaseName = structure(logical(0), tags = list(type = "string")), ClusterIdentifier = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SelectSqlQuery = structure(logical(0), tags = list(type = "string")), DatabaseCredentials = structure(list(Username = structure(logical(0), tags = list(type = "string")), Password = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), S3StagingLocation = structure(logical(0), tags = list(type = "string")), DataRearrangement = structure(logical(0), tags = list(type = "string")), DataSchema = structure(logical(0), tags = list(type = "string")), DataSchemaUri = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RoleARN = structure(logical(0), tags = list(type = "string")), ComputeStatistics = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_redshift_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_s3_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataSourceName = structure(logical(0), tags = list(type = "string")), DataSpec = structure(list(DataLocationS3 = structure(logical(0), tags = list(type = "string")), DataRearrangement = structure(logical(0), tags = list(type = "string")), DataSchema = structure(logical(0), tags = list(type = "string")), DataSchemaLocationS3 = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), ComputeStatistics = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_data_source_from_s3_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_evaluation_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string")), EvaluationName = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), EvaluationDataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_evaluation_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_ml_model_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), MLModelName = structure(logical(0), tags = list(type = "string")), MLModelType = structure(logical(0), tags = list(type = "string")), Parameters = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), TrainingDataSourceId = structure(logical(0), tags = list(type = "string")), Recipe = structure(logical(0), tags = list(type = "string")), RecipeUri = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_ml_model_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_realtime_endpoint_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$create_realtime_endpoint_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), RealtimeEndpointInfo = structure(list(PeakRequestsPerSecond = structure(logical(0), tags = list(type = "integer")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), EndpointUrl = structure(logical(0), tags = list(type = "string")), EndpointStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_batch_prediction_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_batch_prediction_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_data_source_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_data_source_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_evaluation_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_evaluation_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_ml_model_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_ml_model_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_realtime_endpoint_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_realtime_endpoint_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), RealtimeEndpointInfo = structure(list(PeakRequestsPerSecond = structure(logical(0), tags = list(type = "integer")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), EndpointUrl = structure(logical(0), tags = list(type = "string")), EndpointStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_tags_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(TagKeys = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$delete_tags_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_batch_predictions_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(FilterVariable = structure(logical(0), tags = list(type = "string")), EQ = structure(logical(0), tags = list(type = "string")), GT = structure(logical(0), tags = list(type = "string")), LT = structure(logical(0), tags = list(type = "string")), GE = structure(logical(0), tags = list(type = "string")), LE = structure(logical(0), tags = list(type = "string")), NE = structure(logical(0), tags = list(type = "string")), Prefix = structure(logical(0), tags = list(type = "string")), SortOrder = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_batch_predictions_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Results = structure(list(structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), BatchPredictionDataSourceId = structure(logical(0), tags = list(type = "string")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), OutputUri = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp")), TotalRecordCount = structure(logical(0), tags = list(type = "long")), InvalidRecordCount = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_data_sources_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(FilterVariable = structure(logical(0), tags = list(type = "string")), EQ = structure(logical(0), tags = list(type = "string")), GT = structure(logical(0), tags = list(type = "string")), LT = structure(logical(0), tags = list(type = "string")), GE = structure(logical(0), tags = list(type = "string")), LE = structure(logical(0), tags = list(type = "string")), NE = structure(logical(0), tags = list(type = "string")), Prefix = structure(logical(0), tags = list(type = "string")), SortOrder = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_data_sources_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Results = structure(list(structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataLocationS3 = structure(logical(0), tags = list(type = "string")), DataRearrangement = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), DataSizeInBytes = structure(logical(0), tags = list(type = "long")), NumberOfFiles = structure(logical(0), tags = list(type = "long")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), RedshiftMetadata = structure(list(RedshiftDatabase = structure(list(DatabaseName = structure(logical(0), tags = list(type = "string")), ClusterIdentifier = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), DatabaseUserName = structure(logical(0), tags = list(type = "string")), SelectSqlQuery = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RDSMetadata = structure(list(Database = structure(list(InstanceIdentifier = structure(logical(0), tags = list(type = "string")), DatabaseName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), DatabaseUserName = structure(logical(0), tags = list(type = "string")), SelectSqlQuery = structure(logical(0), tags = list(type = "string")), ResourceRole = structure(logical(0), tags = list(type = "string")), ServiceRole = structure(logical(0), tags = list(type = "string")), DataPipelineId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RoleARN = structure(logical(0), tags = list(type = "string")), ComputeStatistics = structure(logical(0), tags = list(type = "boolean")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_evaluations_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(FilterVariable = structure(logical(0), tags = list(type = "string")), EQ = structure(logical(0), tags = list(type = "string")), GT = structure(logical(0), tags = list(type = "string")), LT = structure(logical(0), tags = list(type = "string")), GE = structure(logical(0), tags = list(type = "string")), LE = structure(logical(0), tags = list(type = "string")), NE = structure(logical(0), tags = list(type = "string")), Prefix = structure(logical(0), tags = list(type = "string")), SortOrder = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_evaluations_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Results = structure(list(structure(list(EvaluationId = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), EvaluationDataSourceId = structure(logical(0), tags = list(type = "string")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), PerformanceMetrics = structure(list(Properties = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_ml_models_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(FilterVariable = structure(logical(0), tags = list(type = "string")), EQ = structure(logical(0), tags = list(type = "string")), GT = structure(logical(0), tags = list(type = "string")), LT = structure(logical(0), tags = list(type = "string")), GE = structure(logical(0), tags = list(type = "string")), LE = structure(logical(0), tags = list(type = "string")), NE = structure(logical(0), tags = list(type = "string")), Prefix = structure(logical(0), tags = list(type = "string")), SortOrder = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_ml_models_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Results = structure(list(structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), TrainingDataSourceId = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), SizeInBytes = structure(logical(0), tags = list(type = "long")), EndpointInfo = structure(list(PeakRequestsPerSecond = structure(logical(0), tags = list(type = "integer")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), EndpointUrl = structure(logical(0), tags = list(type = "string")), EndpointStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TrainingParameters = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), Algorithm = structure(logical(0), tags = list(type = "string")), MLModelType = structure(logical(0), tags = list(type = "string")), ScoreThreshold = structure(logical(0), tags = list(type = "float")), ScoreThresholdLastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_tags_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$describe_tags_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), ResourceType = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_batch_prediction_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_batch_prediction_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), BatchPredictionDataSourceId = structure(logical(0), tags = list(type = "string")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), OutputUri = structure(logical(0), tags = list(type = "string")), LogUri = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp")), TotalRecordCount = structure(logical(0), tags = list(type = "long")), InvalidRecordCount = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_data_source_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), Verbose = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_data_source_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataLocationS3 = structure(logical(0), tags = list(type = "string")), DataRearrangement = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), DataSizeInBytes = structure(logical(0), tags = list(type = "long")), NumberOfFiles = structure(logical(0), tags = list(type = "long")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), LogUri = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), RedshiftMetadata = structure(list(RedshiftDatabase = structure(list(DatabaseName = structure(logical(0), tags = list(type = "string")), ClusterIdentifier = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), DatabaseUserName = structure(logical(0), tags = list(type = "string")), SelectSqlQuery = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RDSMetadata = structure(list(Database = structure(list(InstanceIdentifier = structure(logical(0), tags = list(type = "string")), DatabaseName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), DatabaseUserName = structure(logical(0), tags = list(type = "string")), SelectSqlQuery = structure(logical(0), tags = list(type = "string")), ResourceRole = structure(logical(0), tags = list(type = "string")), ServiceRole = structure(logical(0), tags = list(type = "string")), DataPipelineId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), RoleARN = structure(logical(0), tags = list(type = "string")), ComputeStatistics = structure(logical(0), tags = list(type = "boolean")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp")), DataSourceSchema = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_evaluation_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_evaluation_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string")), MLModelId = structure(logical(0), tags = list(type = "string")), EvaluationDataSourceId = structure(logical(0), tags = list(type = "string")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), PerformanceMetrics = structure(list(Properties = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure")), LogUri = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_ml_model_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), Verbose = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$get_ml_model_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), TrainingDataSourceId = structure(logical(0), tags = list(type = "string")), CreatedByIamUser = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), LastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), Name = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), SizeInBytes = structure(logical(0), tags = list(type = "long")), EndpointInfo = structure(list(PeakRequestsPerSecond = structure(logical(0), tags = list(type = "integer")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), EndpointUrl = structure(logical(0), tags = list(type = "string")), EndpointStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TrainingParameters = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), InputDataLocationS3 = structure(logical(0), tags = list(type = "string")), MLModelType = structure(logical(0), tags = list(type = "string")), ScoreThreshold = structure(logical(0), tags = list(type = "float")), ScoreThresholdLastUpdatedAt = structure(logical(0), tags = list(type = "timestamp")), LogUri = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), ComputeTime = structure(logical(0), tags = list(type = "long")), FinishedAt = structure(logical(0), tags = list(type = "timestamp")), StartedAt = structure(logical(0), tags = list(type = "timestamp")), Recipe = structure(logical(0), tags = list(type = "string")), Schema = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$predict_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), Record = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), PredictEndpoint = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$predict_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(Prediction = structure(list(predictedLabel = structure(logical(0), tags = list(type = "string")), predictedValue = structure(logical(0), tags = list(type = "float")), predictedScores = structure(list(structure(logical(0), tags = list(type = "float"))), tags = list(type = "map")), details = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_batch_prediction_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string")), BatchPredictionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_batch_prediction_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(BatchPredictionId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_data_source_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string")), DataSourceName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_data_source_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(DataSourceId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_evaluation_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string")), EvaluationName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_evaluation_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(EvaluationId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_ml_model_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string")), MLModelName = structure(logical(0), tags = list(type = "string")), ScoreThreshold = structure(logical(0), tags = list(type = "float"))), tags = list(type = "structure"))
  return(populate(args, shape))
}

.machinelearning$update_ml_model_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  shape <- structure(list(MLModelId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
  return(populate(args, shape))
}