lookoutequipment_import_model_version: Imports a model that has been trained successfully
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

View source: R/lookoutequipment_operations.R

lookoutequipment_import_model_version

R Documentation

Imports a model that has been trained successfully

Description

Imports a model that has been trained successfully.

See https://www.paws-r-sdk.com/docs/lookoutequipment_import_model_version/ for full documentation.

Usage

lookoutequipment_import_model_version(
  SourceModelVersionArn,
  ModelName = NULL,
  DatasetName,
  LabelsInputConfiguration = NULL,
  ClientToken,
  RoleArn = NULL,
  ServerSideKmsKeyId = NULL,
  Tags = NULL,
  InferenceDataImportStrategy = NULL
)

Arguments

`SourceModelVersionArn`	[required] The Amazon Resource Name (ARN) of the model version to import.
`ModelName`	The name for the machine learning model to be created. If the model already exists, Amazon Lookout for Equipment creates a new version. If you do not specify this field, it is filled with the name of the source model.
`DatasetName`	[required] The name of the dataset for the machine learning model being imported.
`LabelsInputConfiguration`
`ClientToken`	[required] A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
`RoleArn`	The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.
`ServerSideKmsKeyId`	Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.
`Tags`	The tags associated with the machine learning model to be created.
`InferenceDataImportStrategy`	Indicates how to import the accumulated inference data when a model version is imported. The possible values are as follows: NO_IMPORT – Don't import the data. ADD_WHEN_EMPTY – Only import the data from the source model if there is no existing data in the target model. OVERWRITE – Import the data from the source model and overwrite the existing data in the target model.