Question

A data scientist has registered a model as `main.ml.churn_model` (version 3) in Unity Catalog. They want to programmatically create a Databricks Model Serving endpoint from a notebook using the MLflow Deployments SDK so the model can serve real-time requests. Which approach correctly references the Unity Catalog model in the endpoint configuration?

Accepted Answer

Use `get_deploy_client("databricks")` and pass `served_entities` with `entity_name="main.ml.churn_model"` and `entity_version="3"`.

Answer

Use `mlflow.pyfunc.load_model("models:/main.ml.churn_model/3")` and call `.serve()` on the returned model object.

Answer

Register the model to the Workspace Model Registry first, then pass `model_name` and `model_version` to `get_deploy_client("local")`.

Answer

Use `spark_udf` to wrap the model and expose it as a Spark temporary view for streaming queries.