MLflow Model Registry

What MLflow Model Registry is

MLflow Model Registry is a centralized model store, API, and interface for managing machine-learning models throughout their lifecycle. It records versions and connects each to the MLflow run, logged model, or notebook that produced it. Teams can organize models using aliases, tags, and annotations, and use the registry to move models through stages such as Staging and Production. The open-source registry offers model registration, version tracking, descriptions, and stage transitions. On a remote tracking server, MLflow supports basic HTTP authentication and role-based permissions for registered models; basic authentication requires a configured secret key and an admin password of at least 12 characters. With Databricks Unity Catalog, it supports centralized governance, access controls, cross-workspace access, and lineage. An official Helm chart is available for Kubernetes self-hosting. MLflow lists integrations with 100+ tools and support for Python, TypeScript/JavaScript, Java, R, and OpenTelemetry. Its Open Source plan costs nothing under the Apache 2.0 license. Artifact storage is not supported.