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CreateInferenceSchedulerResponse

Source code in docs_src/capo_lookoutequipment/types/create_inference_scheduler_response.py 
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class CreateInferenceSchedulerResponse(TypedDict, closed=True):
    inference_scheduler_arn: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_arn.InferenceSchedulerArn"
    ]
    """<p>The Amazon Resource Name (ARN) of the inference scheduler being created. </p>"""
    inference_scheduler_name: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_name.InferenceSchedulerName"
    ]
    """<p>The name of inference scheduler being created. </p>"""
    status: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_status.InferenceSchedulerStatus"
    ]
    """<p>Indicates the status of the <code>CreateInferenceScheduler</code> operation. </p>"""
    model_quality: NotRequired["capo_lookoutequipment.types.model_quality.ModelQuality"]
    """<p>Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is <code>POOR_QUALITY_DETECTED</code>. Otherwise, the value is <code>QUALITY_THRESHOLD_MET</code>. </p> <p>If the model is unlabeled, the model quality can't be assessed and the value of <code>ModelQuality</code> is <code>CANNOT_DETERMINE_QUALITY</code>. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model.</p> <p>For information about using labels with your models, see <a href="https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html">Understanding labeling</a>.</p> <p>For information about improving the quality of a model, see <a href="https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html">Best practices with Amazon Lookout for Equipment</a>.</p>"""

inference_scheduler_arn instance-attribute 

inference_scheduler_arn: NotRequired[InferenceSchedulerArn]

The Amazon Resource Name (ARN) of the inference scheduler being created.

inference_scheduler_name instance-attribute 

inference_scheduler_name: NotRequired[InferenceSchedulerName]

The name of inference scheduler being created.

model_quality instance-attribute 

model_quality: NotRequired[ModelQuality]

Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is POOR_QUALITY_DETECTED. Otherwise, the value is QUALITY_THRESHOLD_MET.

If the model is unlabeled, the model quality can't be assessed and the value of ModelQuality is CANNOT_DETERMINE_QUALITY. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model.

For information about using labels with your models, see Understanding labeling.

For information about improving the quality of a model, see Best practices with Amazon Lookout for Equipment.

status instance-attribute 

status: NotRequired[InferenceSchedulerStatus]

Indicates the status of the CreateInferenceScheduler operation.