Move models through customizable staging environments (Development, Staging, Production, or any stage alias you choose) with built-in approval workflow capabilities and automated notifications. Maintain complete audit trails of model transitions with detailed metadata about who approved changes and when they occurred.

Deploy models as Docker containers, Python functions, REST endpoints, or directly to various serving platforms with MLflow's versatile deployment capabilities. Streamline the transition from development to production with consistent model behavior across any target environment, from local testing to cloud-based serving.