UpdateRetrainingSchedulerRequest ¶

class UpdateRetrainingSchedulerRequest(TypedDict, closed=True):
    model_name: "capo_lookoutequipment.types.model_name.ModelName"
    """<p>The name of the model whose retraining scheduler you want to update. </p>"""
    retraining_start_date: NotRequired[
        "capo_lookoutequipment.types.timestamp.Timestamp"
    ]
    """<p>The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.</p>"""
    retraining_frequency: NotRequired[
        "capo_lookoutequipment.types.retraining_frequency.RetrainingFrequency"
    ]
    """<p>This parameter uses the <a href="https://en.wikipedia.org/wiki/ISO_8601#Durations">ISO 8601</a> standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:</p> <ul> <li> <p>P3M15D – Every 3 months and 15 days</p> </li> <li> <p>P2M – Every 2 months</p> </li> <li> <p>P150D – Every 150 days</p> </li> </ul>"""
    lookback_window: NotRequired[
        "capo_lookoutequipment.types.lookback_window.LookbackWindow"
    ]
    """<p>The number of past days of data that will be used for retraining.</p>"""
    promote_mode: NotRequired[
        "capo_lookoutequipment.types.model_promote_mode.ModelPromoteMode"
    ]
    """<p>Indicates how the service will use new models. In <code>MANAGED</code> mode, new models will automatically be used for inference if they have better performance than the current model. In <code>MANUAL</code> mode, the new models will not be used <a href="https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/versioning-model.html#model-activation">until they are manually activated</a>.</p>"""