This feature is in Public Preview.
This page describes how to use the Model Registry with Unity Catalog. MLflow Model Registry is a centralized model repository and a UI and set of APIs that enable you to manage the full lifecycle of MLflow Models. Databricks provides a hosted version of the MLflow Model Registry in Unity Catalog. You can also use the Workspace Model Registry.
See Manage model lifecycle in Unity Catalog for additional information about using the Model Registry and Unity Catalog to manage the model lifecycle.
Model: An MLflow Model logged from an experiment or run that is logged with one of the model flavor’s
mlflow.<model-flavor>.log_modelmethods. Once logged, you can register the model with the Model Registry.
Registered model: An MLflow Model that has been registered with the Model Registry. The registered model has a unique name, versions, model lineage, and other metadata.
Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.
Model alias: An alias is a mutable, named reference to a particular version of a registered model. Typical uses of aliases are to specify which model versions are deployed in a given environment in your model training workflows or to write inference workloads that target a specific alias. For example, you could assign the “Champion” alias of your “Fraud Detection” registered model to the model version that should serve the majority of production traffic, and then write inference workloads that target that alias (that is, make predictions using the “Champion” version).
Model stage: A model version can be assigned one or more stages. MLflow provides predefined stages for the common use-cases None, Staging, Production, and Archived. With the appropriate permission you can transition a model version between stages or you can request a model stage transition. Model version stages are supported only in the classic Workspace Model Registry.
Description: You can annotate a model’s intent, including description and any relevant information useful for the team such as algorithm description, dataset employed, or methodology.
For an example that illustrates how to use the Model Registry to build a machine learning application that forecasts the daily power output of a wind farm, see: