Databricks Machine Learning Professional Question 73
Select 3A data science team is working on a machine learning project and needs a centralized system to manage their models across development, staging, and production phases. They want to track model versions, register new models, and transition models between stages. Which of the following actions can they perform using the Databricks Model Registry?
- A
Track different versions of a machine learning model.
- B
Deploy models directly to production environments without any external integration.
- C
Transition models between various lifecycle stages such as 'Staging' and 'Production'.
- D
Record and view metadata, such as the model's training parameters and metrics.
- E
Automatically generate training datasets for new models.
Show answer and explanation
Correct answers: A, C, D
Explanation
The Databricks Model Registry is a centralized repository that enables users to manage machine learning models. Key functionalities include tracking model versions, managing lifecycle stages, and storing metadata. However, it does not handle tasks like automatic dataset generation or direct deployment to production, as these require additional tools or integrations.
- A. Correct.
Correct: The Databricks Model Registry allows users to track multiple versions of a model, enabling better organization and traceability.
- B. Incorrect.
Incorrect: While the Model Registry helps manage models, deploying them directly to production environments requires integration with serving endpoints or other tools.
- C. Correct.
Correct: The Model Registry provides lifecycle management capabilities, allowing users to transition models between stages like 'Staging' and 'Production'.
- D. Correct.
Correct: The Model Registry enables users to store and view metadata, such as training parameters, metrics, and other relevant details about the model.
- E. Incorrect.
Incorrect: The Model Registry does not automatically generate training datasets. Dataset creation is typically handled during the data preparation or feature engineering stages.