create_data_quality_job_definition
async
¶
create_data_quality_job_definition(
*,
config_overrides: Optional[AsyncSageMakerClientConfig] = None,
job_definition_name: Optional[MonitoringJobDefinitionName] = None,
data_quality_baseline_config: Optional[DataQualityBaselineConfig] = None,
data_quality_app_specification: Optional[DataQualityAppSpecification] = None,
data_quality_job_input: Optional[DataQualityJobInput] = None,
data_quality_job_output_config: Optional[MonitoringOutputConfig] = None,
job_resources: Optional[MonitoringResources] = None,
network_config: Optional[MonitoringNetworkConfig] = None,
role_arn: Optional[RoleArn] = None,
stopping_condition: Optional[MonitoringStoppingCondition] = None,
tags: Optional[TagList] = None,
) -> CreateDataQualityJobDefinitionResponse
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
Parameters:
-
job_definition_name(Optional[MonitoringJobDefinitionName], default:None) –The name for the monitoring job definition.
-
data_quality_baseline_config(Optional[DataQualityBaselineConfig], default:None) –Configures the constraints and baselines for the monitoring job.
-
data_quality_app_specification(Optional[DataQualityAppSpecification], default:None) –Specifies the container that runs the monitoring job.
-
data_quality_job_input(Optional[DataQualityJobInput], default:None) –A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
-
network_config(Optional[MonitoringNetworkConfig], default:None) –Specifies networking configuration for the monitoring job.
-
role_arn(Optional[RoleArn], default:None) –The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
-
tags(Optional[TagList], default:None) –(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Raises:
-
ResourceInUse–Resource being accessed is in use.
-
ResourceLimitExceeded–You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
UnknownServiceError–The service returned an error code this client does not model.
Source code in docs_src/capo_sagemaker/_services/async_sage_maker.py
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