Question

A data scientist has trained a scikit-learn classifier and logged it as an artifact within an MLflow run. The team now wants to register this model in Unity Catalog so that downstream consumers can reference it by a three-level name (catalog.schema.model) and automatically capture model lineage back to the originating run. Which approach correctly registers the model into Unity Catalog?

Accepted Answer

Set the MLflow registry URI to 'databricks-uc', then call mlflow.register_model() with the run's model URI (e.g., 'runs:/<run_id>/model') and a three-level name like 'main.ml_team.churn_model'.

Answer

Call mlflow.register_model() using only a two-level name 'ml_team.churn_model' while the registry URI remains set to the default workspace registry.

Answer

Manually copy the model artifact files from DBFS into a Unity Catalog volume, since Unity Catalog cannot track MLflow model versions directly.

Answer

Use mlflow.log_model() with the registered_model_name parameter set to just 'churn_model', which will register it in Unity Catalog by default.