Recommender ¶

class Recommender(TypedDict, closed=True):
    recommender_arn: NotRequired["capo_personalize.types.arn.Arn"]
    """<p>The Amazon Resource Name (ARN) of the recommender.</p>"""
    dataset_group_arn: NotRequired["capo_personalize.types.arn.Arn"]
    """<p>The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.</p>"""
    name: NotRequired["capo_personalize.types.name.Name"]
    """<p>The name of the recommender.</p>"""
    recipe_arn: NotRequired["capo_personalize.types.arn.Arn"]
    """<p>The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for. </p>"""
    recommender_config: NotRequired[
        "capo_personalize.types.recommender_config.RecommenderConfig"
    ]
    """<p>The configuration details of the recommender.</p>"""
    creation_date_time: NotRequired["capo_personalize.types.date.Date"]
    """<p>The date and time (in Unix format) that the recommender was created.</p>"""
    last_updated_date_time: NotRequired["capo_personalize.types.date.Date"]
    """<p>The date and time (in Unix format) that the recommender was last updated.</p>"""
    status: NotRequired["capo_personalize.types.status.Status"]
    """<p>The status of the recommender.</p> <p>A recommender can be in one of the following states:</p> <ul> <li> <p>CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED</p> </li> <li> <p>STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE</p> </li> <li> <p>DELETE PENDING > DELETE IN_PROGRESS</p> </li> </ul>"""
    failure_reason: NotRequired["capo_personalize.types.failure_reason.FailureReason"]
    """<p>If a recommender fails, the reason behind the failure.</p>"""
    latest_recommender_update: NotRequired[
        "capo_personalize.types.recommender_update_summary.RecommenderUpdateSummary"
    ]
    """<p>Provides a summary of the latest updates to the recommender. </p>"""
    model_metrics: NotRequired["capo_personalize.types.metrics.Metrics"]
    """<p>Provides evaluation metrics that help you determine the performance of a recommender. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/evaluating-recommenders.html"> Evaluating a recommender</a>.</p>"""