Microsoft ' s documentation on Azure Machine Learning model monitoring describes a layered alerting architecture. At the base layer, Azure ML model monitors compute drift, prediction, and data quality metrics on a scheduled basis and publish results as Azure Monitor metrics. To notify stakeholders, you create an Azure Monitor alert rule that watches these metrics and fires an action group when a threshold is breached. Action groups support email, SMS, push notifications, and webhook calls. To initiate automated retraining, the webhook call in the action group targets an Azure ML pipeline ' s REST endpoint, triggering a retraining run. Alternatively, Azure Event Grid subscriptions on AML workspace events can route model-quality events to Azure Functions that start pipelines. The separation of monitoring, alerting, notification, and remediation is intentional, allowing each component to be updated independently.

Microsoft Learn Reference Topic: Set up model monitoring for data and model quality – Azure Machine Learning model monitoring