Databricks Machine Learning Associate Question 406
Select 4A data science team is deploying a machine learning model into production using Databricks. As part of their MLOps strategy, they want to ensure model versioning, reproducibility, and continuous monitoring. Which practices should they prioritize to align with MLOps best practices?
- A
Use MLflow to track model versions, parameters, and metrics.
- B
Store models in a version-controlled repository like a Git-based system.
- C
Only retrain the model manually when performance drops below a threshold.
- D
Automate the deployment pipeline to test and promote models to production.
- E
Monitor production models for drift and retrain them periodically.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
MLOps best practices emphasize automation, reproducibility, and monitoring throughout the machine learning lifecycle. Using tools like MLflow ensures proper tracking and versioning of models, while automated pipelines and monitoring enable robust and scalable model management. Manual interventions should be minimized to reduce errors and improve efficiency.
- A. Correct.
Correct: MLflow is a robust tool integrated with Databricks that allows teams to track models, parameters, and metrics, which is essential for reproducibility and versioning in MLOps.
- B. Correct.
Correct: Storing models in a version-controlled repository ensures that all changes can be tracked and reverted if necessary, aligning with MLOps best practices.
- C. Incorrect.
Incorrect: While retraining models is necessary, relying solely on manual retraining is not a best practice. Instead, MLOps encourages automated retraining pipelines based on predefined triggers.
- D. Correct.
Correct: Automating the deployment pipeline allows for consistent testing, validation, and promotion of models, reducing the risk of human error and improving efficiency.
- E. Correct.
Correct: Monitoring production models for drift and retraining them periodically ensures that models remain accurate and relevant as data distributions change over time.