Databricks Machine Learning Associate Question 3
Select 3You are working on a machine learning project in Databricks and need to track the performance of several models across different experiments. Which of the following actions can you take to effectively manage and compare these models using Databricks Machine Learning features?
- A
Use MLflow to log model parameters, metrics, and artifacts within Databricks.
- B
Register the best-performing models in the Databricks Model Registry for version control and deployment.
- C
Manually keep track of model details in a separate notebook for better comparison.
- D
Leverage the MLflow Experiment dashboard in Databricks to visualize and compare experiment results.
- E
Export models to external storage for comparison outside of Databricks.
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks Machine Learning provides a suite of tools, including MLflow and the Model Registry, to streamline model tracking, comparison, and deployment. Logging experiments with MLflow and using the Experiment dashboard enables efficient and scalable management of machine learning workflows. The Model Registry further enhances this by providing version control and deployment capabilities.
- A. Correct.
Correct: MLflow is fully integrated into Databricks and allows you to log parameters, metrics, and artifacts for tracking and managing experiments.
- B. Correct.
Correct: The Databricks Model Registry is specifically designed to manage model versions, deploy models, and maintain a history of changes.
- C. Incorrect.
Incorrect: Manually tracking model details is not only inefficient but also error-prone, especially when Databricks provides built-in tools like MLflow for this purpose.
- D. Correct.
Correct: The MLflow Experiment dashboard in Databricks provides a centralized interface to visualize and compare experiment results, making it easier to analyze performance.
- E. Incorrect.
Incorrect: While exporting models for external comparison is possible, it is not an effective or recommended approach when Databricks offers native tools for tracking and comparison.