Diving into the webhooks support in the Model Registry

A webhook allows users to create custom callbacks to enable communication between web applications. Webhooks allow a system to push data into another system automatically when some event occurs.

As an example, this could apply if you want to automatically trigger a notification on Slack when you detect a new transition request for a model in MLflow, or if you want to trigger a new model build when there is a new code commit in your version control branch.

MLflow webhooks provide capabilities for end users to automatically listen to any events related to the Model Registry and trigger actions. The webhooks can be integrated with messaging systems such as Slack to send notifications or trigger CI/CD pipelines for automatically testing and deploying ML models.

You can use webhooks using the Python client or Databricks REST API.

There are two different types of webhooks that are supported by the MLflow Model Registry based...