MLflow Model Registry

MLflow Model Registry is a tool that collaboratively manages the life cycle of all the MLflow Models in a centralized manner across an organization. In Databricks, the integrated Model Registry provides granular access control over who can transition models from one stage to another.

MLflow Model Registry allows multiple versions of the models in a particular stage. It enables the transition of the best-suited model between staging, prod, and archived states either programmatically or by a human-in-the-loop deployment model. Choosing one strategy over another for model deployment will depend on the use case and how comfortable teams are in automating the entire process of managing ML model promotion and testing process. We will take a deeper look into this in Chapter 6, Model Versioning and Webhooks.

Model Registry also logs model descriptions, lineage, and promotion activity from one stage to another, providing full traceability.

We will look into the...