Databricks Machine Learning Associate Question 403
Select 3A data science team is deploying a machine learning model into production using Databricks. To enable a robust MLOps strategy, which of the following practices should they adopt?
- A
Version control both the code and data used for training the model.
- B
Manually track model performance metrics in a shared spreadsheet.
- C
Implement CI/CD pipelines for automated testing and deployment of models.
- D
Integrate model monitoring to track drift and performance in production.
- E
Deploy the model without any rollback strategy to speed up deployment time.
Show answer and explanation
Correct answers: A, C, D
Explanation
MLOps strategies focus on automation, reliability, and scalability in the machine learning lifecycle. Practices like version control, CI/CD pipelines, and model monitoring ensure reproducibility, efficiency, and consistent performance in production. Manual processes or risky shortcuts, such as skipping rollback strategies, are contrary to MLOps principles and can lead to operational failures.
- A. Correct.
Version control for both code and data ensures reproducibility and traceability of models, which is a core MLOps best practice.
- B. Incorrect.
Manually tracking metrics is error-prone and not scalable, making it unsuitable for a robust MLOps strategy.
- C. Correct.
CI/CD pipelines automate testing and deployment, reducing manual errors and improving efficiency, which aligns with MLOps principles.
- D. Correct.
Model monitoring is crucial for detecting drift and maintaining model performance over time in production, a key component of MLOps.
- E. Incorrect.
Skipping a rollback strategy increases risk and goes against MLOps best practices, which emphasize reliability and contingency planning.