class Aws::LookoutEquipment::Types::CreateModelRequest
@note When making an API call, you may pass CreateModelRequest
data as a hash: { model_name: "ModelName", # required dataset_name: "DatasetIdentifier", # required dataset_schema: { inline_data_schema: "InlineDataSchema", }, labels_input_configuration: { s3_input_configuration: { # required bucket: "S3Bucket", # required prefix: "S3Prefix", }, }, client_token: "IdempotenceToken", # required training_data_start_time: Time.now, training_data_end_time: Time.now, evaluation_data_start_time: Time.now, evaluation_data_end_time: Time.now, role_arn: "IamRoleArn", data_pre_processing_configuration: { target_sampling_rate: "PT1S", # accepts PT1S, PT5S, PT10S, PT15S, PT30S, PT1M, PT5M, PT10M, PT15M, PT30M, PT1H }, server_side_kms_key_id: "NameOrArn", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], off_condition: "OffCondition", }
@!attribute [rw] model_name
The name for the ML model to be created. @return [String]
@!attribute [rw] dataset_name
The name of the dataset for the ML model being created. @return [String]
@!attribute [rw] dataset_schema
The data schema for the ML model being created. @return [Types::DatasetSchema]
@!attribute [rw] labels_input_configuration
The input configuration for the labels being used for the ML model that's being created. @return [Types::LabelsInputConfiguration]
@!attribute [rw] client_token
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one. **A suitable default value is auto-generated.** You should normally not need to pass this option. @return [String]
@!attribute [rw] training_data_start_time
Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model. @return [Time]
@!attribute [rw] training_data_end_time
Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model. @return [Time]
@!attribute [rw] evaluation_data_start_time
Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model. @return [Time]
@!attribute [rw] evaluation_data_end_time
Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model. @return [Time]
@!attribute [rw] role_arn
The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model. @return [String]
@!attribute [rw] data_pre_processing_configuration
The configuration is the `TargetSamplingRate`, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the `TargetSamplingRate` is 1 minute. When providing a value for the `TargetSamplingRate`, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore *PT1S*, the value for a 15 minute rate is *PT15M*, and the value for a 1 hour rate is *PT1H* @return [Types::DataPreProcessingConfiguration]
@!attribute [rw] server_side_kms_key_id
Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment. @return [String]
@!attribute [rw] tags
Any tags associated with the ML model being created. @return [Array<Types::Tag>]
@!attribute [rw] off_condition
Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference. @return [String]
@see docs.aws.amazon.com/goto/WebAPI/lookoutequipment-2020-12-15/CreateModelRequest AWS API Documentation
Constants
- SENSITIVE