Question

You maintain a fraud-detection model deployed to a managed online endpoint in Azure Machine Learning. A model monitor already computes prediction drift and feature-attribution drift on a recurring schedule. The business requires that when the monitored metric exceeds a defined threshold, a retraining pipeline is automatically kicked off without a data scientist manually reviewing the results first. Which approach best implements this automated retraining trigger?

Accepted Answer

Configure an Azure Event Grid subscription on the workspace that reacts to the model monitor's threshold-exceeded event and triggers the training pipeline via an Azure Logic App or Function.

Answer

Increase the model monitor's scheduling frequency to hourly so that retraining occurs automatically on every monitoring run.

Answer

Enable the responsible AI dashboard on the endpoint so that it retrains the model whenever fairness metrics degrade.

Answer

Set the deployment's traffic percentage to 0 when drift is detected so the endpoint automatically falls back to the previous model version.