Q1
Walk me through how you would design a CI/CD pipeline for deploying machine learning models to production. What tools would you use, and how would you handle model versioning and rollback?
Why they ask this:* This tests your understanding of core MLOps infrastructure, your familiarity with deployment orchestration tools (Jenkins, GitLab CI, GitHub Actions), and your ability to design systems that balance speed with safety in model deployments.
Q2
Describe your experience with model monitoring and drift detection in production. What metrics would you track, and how would you automate retraining triggers when model performance degrades?
Why they ask this:* This assesses whether you understand the full lifecycle of deployed models beyond initial training, your knowledge of monitoring tools (Evidently, WhyLabs, Grafana), and your ability to maintain model reliability over time.
Q3
Explain how you would containerize a machine learning application using Docker and orchestrate it with Kubernetes. What challenges have you encountered, and how did you solve them?
Why they ask this:* This evaluates your hands-on experience with containerization and orchestration—essential for scalable MLOps—and your ability to troubleshoot real-world deployment issues in production environments.
Q4
How do you approach managing ML experiment tracking, hyperparameter tuning, and ensuring reproducibility across your team? What tools or frameworks have you used?