Databricks Machine Learning Professional Question 74
Select 3You are working as a data scientist at a company and have trained multiple machine learning models using Databricks. You want to collaborate with your team to evaluate these models, assign staging levels (e.g., 'Staging' or 'Production'), and track the lineage and versioning of these models. Which Databricks feature would you use, and what are the primary actions you can perform with it?
- A
Use the Model Registry to register models, assign them to stages like 'Staging' or 'Production', and manage model versions.
- B
Use the Model Registry to deploy models directly into production without any external tools.
- C
Use the Model Registry to track the lineage of models, including which code or data was used to train a version.
- D
Use the Model Registry to compare hyperparameter tuning results across different models.
- E
Use the Model Registry to enable team collaboration by adding comments or updating model stage transitions.
Show answer and explanation
Correct answers: A, C, E
Explanation
The Model Registry in Databricks is a key feature for managing machine learning model lifecycles. It enables users to register models, assign them to stages like 'Staging' or 'Production,' track lineage, and collaborate within teams. However, it does not directly handle model deployment or hyperparameter comparison, as these functionalities are outside the scope of the Model Registry.
- A. Correct.
Correct. The Model Registry is designed to register models, assign them to stages such as 'Staging' or 'Production,' and handle versioning of models effectively.
- B. Incorrect.
Incorrect. While the Model Registry helps manage and organize models, it does not directly deploy models into production. Deployment typically involves additional tools or integration with serving endpoints.
- C. Correct.
Correct. The Model Registry tracks model lineage, including details about the code, data, and environment used to produce specific model versions.
- D. Incorrect.
Incorrect. The Model Registry is not specifically designed for comparing hyperparameter tuning results. Tools like MLflow Tracking handle experiment tracking and hyperparameter comparisons.
- E. Correct.
Correct. The Model Registry supports team collaboration by allowing users to add comments and update the status of models as they progress through different stages, ensuring transparency and coordination.