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Answer: Use the Databricks Model Registry to register the model and select "Unity Catalog" as the destination., Use the MLflow `mlflow.register_model()` function with the Unity Catalog URI.
To properly register a machine learning model in Unity Catalog after training it with MLflow in Databricks, the correct steps involve using the Databricks Model Registry and MLflow functions that interface with Unity Catalog: - **B.** The Databricks Model Registry allows you to manage, discover, and version your models. When registering a model, you can specify "Unity Catalog" as the destination. - **E.** You can use the MLflow `mlflow.register_model()` function, providing the Unity Catalog URI to ensure the model is registered in Unity Catalog. These steps ensure seamless integration and proper registration within the Unity Catalog ecosystem.
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Question: 2 You have successfully trained a machine learning model in Databricks using MLflow. Your next task is to register the model to Unity Catalog for easy discovery and management. What are the correct steps you should take to ensure the model is properly registered? (Select two)
A
Tag the model with a Unity Catalog-specific tag using mlflow.set_tag() before registering it.
B
Use the Databricks Model Registry to register the model and select "Unity Catalog" as the destination.
C
Register the model manually by navigating to the Unity Catalog tab in the Databricks workspace.
D
Set the environment variable MLFLOW_MODEL_REGISTRY_URI to the Unity Catalog URI before running your MLflow script.
E
Use the MLflow mlflow.register_model() function with the Unity Catalog URI.