Skip to content

InferenceSchedulerSummary

Source code in docs_src/capo_lookoutequipment/types/inference_scheduler_summary.py
class InferenceSchedulerSummary(TypedDict, closed=True):
    model_name: NotRequired["capo_lookoutequipment.types.model_name.ModelName"]
    """<p>The name of the machine learning model used for the inference scheduler. </p>"""
    model_arn: NotRequired["capo_lookoutequipment.types.model_arn.ModelArn"]
    """<p> The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler. </p>"""
    inference_scheduler_name: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_name.InferenceSchedulerName"
    ]
    """<p>The name of the inference scheduler. </p>"""
    inference_scheduler_arn: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_arn.InferenceSchedulerArn"
    ]
    """<p> The Amazon Resource Name (ARN) of the inference scheduler. </p>"""
    status: NotRequired[
        "capo_lookoutequipment.types.inference_scheduler_status.InferenceSchedulerStatus"
    ]
    """<p>Indicates the status of the inference scheduler. </p>"""
    data_delay_offset_in_minutes: NotRequired[
        "capo_lookoutequipment.types.data_delay_offset_in_minutes.DataDelayOffsetInMinutes"
    ]
    """<p>A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if an offset delay time of five minutes was selected, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data. </p>"""
    data_upload_frequency: NotRequired[
        "capo_lookoutequipment.types.data_upload_frequency.DataUploadFrequency"
    ]
    """<p>How often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes. </p>"""
    latest_inference_result: NotRequired[
        "capo_lookoutequipment.types.latest_inference_result.LatestInferenceResult"
    ]
    """<p>Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).</p>"""

data_delay_offset_in_minutes instance-attribute

data_delay_offset_in_minutes: NotRequired[DataDelayOffsetInMinutes]

A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if an offset delay time of five minutes was selected, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.

data_upload_frequency instance-attribute

data_upload_frequency: NotRequired[DataUploadFrequency]

How often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.

inference_scheduler_arn instance-attribute

inference_scheduler_arn: NotRequired[InferenceSchedulerArn]

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

inference_scheduler_name instance-attribute

inference_scheduler_name: NotRequired[InferenceSchedulerName]

The name of the inference scheduler.

latest_inference_result instance-attribute

latest_inference_result: NotRequired[LatestInferenceResult]

Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).

model_arn instance-attribute

model_arn: NotRequired[ModelArn]

The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler.

model_name instance-attribute

model_name: NotRequired[ModelName]

The name of the machine learning model used for the inference scheduler.

status instance-attribute

status: NotRequired[InferenceSchedulerStatus]

Indicates the status of the inference scheduler.