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