CreateAutoMLJobRequest ¶

class CreateAutoMLJobRequest(TypedDict, closed=True):
    auto_ml_job_name: NotRequired["capo_sagemaker.types.auto_ml_job_name.AutoMLJobName"]
    """<p>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>"""
    input_data_config: NotRequired[
        "capo_sagemaker.types.auto_ml_input_data_config.AutoMLInputDataConfig"
    ]
    """<p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>"""
    output_data_config: NotRequired[
        "capo_sagemaker.types.auto_ml_output_data_config.AutoMLOutputDataConfig"
    ]
    """<p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>"""
    problem_type: NotRequired["capo_sagemaker.types.problem_type.ProblemType"]
    """<p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> SageMaker Autopilot problem types</a>.</p>"""
    auto_ml_job_objective: NotRequired[
        "capo_sagemaker.types.auto_ml_job_objective.AutoMLJobObjective"
    ]
    """<p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>"""
    auto_ml_job_config: NotRequired[
        "capo_sagemaker.types.auto_ml_job_config.AutoMLJobConfig"
    ]
    """<p>A collection of settings used to configure an AutoML job.</p>"""
    role_arn: NotRequired["capo_sagemaker.types.role_arn.RoleArn"]
    """<p>The ARN of the role that is used to access the data.</p>"""
    generate_candidate_definitions_only: NotRequired[
        "capo_sagemaker.types.generate_candidate_definitions_only.GenerateCandidateDefinitionsOnly"
    ]
    """<p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>"""
    tags: NotRequired["capo_sagemaker.types.tag_list.TagList"]
    """<p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web ServicesResources</a>. Tag keys must be unique per resource.</p>"""
    model_deploy_config: NotRequired[
        "capo_sagemaker.types.model_deploy_config.ModelDeployConfig"
    ]
    """<p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>"""