Databricks Machine Learning Associate Question 405
Select 3Your team is developing a machine learning pipeline in Databricks and wants to implement an MLOps strategy to ensure model reliability, reproducibility, and scalability. Which of the following practices align with the best practices of an MLOps strategy?
- A
Implementing version control for both code and model artifacts
- B
Deploying a model directly to production without testing to reduce time-to-market
- C
Using automated CI/CD pipelines for model training, testing, and deployment
- D
Monitoring model performance in production and retraining as necessary
- E
Relying entirely on manual processes to track data changes during experimentation
Show answer and explanation
Correct answers: A, C, D
Explanation
MLOps best practices emphasize automation, reproducibility, monitoring, and scalability in machine learning workflows. Implementing version control, automating pipelines, and monitoring production models align with these principles, while skipping testing or relying on manual tracking introduces risks and inefficiencies.
- A. Correct.
Version control for both code and model artifacts ensures traceability and reproducibility, which are core components of MLOps best practices.
- B. Incorrect.
Deploying a model without testing is a risky practice and goes against MLOps principles, as it compromises reliability and robustness.
- C. Correct.
Automated CI/CD pipelines streamline the process of training, testing, and deploying models, ensuring consistent and scalable workflows.
- D. Correct.
Monitoring production model performance and retraining as necessary is crucial for maintaining model freshness and addressing data drift.
- E. Incorrect.
Relying on manual processes to track data changes increases the chance of errors and inefficiencies, which contradicts the automation focus of MLOps.