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:* They want to assess your understanding of MLOps infrastructure, deployment automation, and your ability to manage the full lifecycle of model releases in production environments.
Q2
Explain your experience with model monitoring and observability. How would you detect model drift, and what metrics would you track to ensure a deployed model remains performant?
Why they ask this:* This tests your knowledge of post-deployment model health, your ability to identify when models degrade, and your understanding of critical MLOps responsibilities beyond initial deployment.
Q3
Describe how you would containerize a machine learning application using Docker and orchestrate it with Kubernetes. What challenges have you encountered with scaling ML workloads?
Why they ask this:* They're evaluating your hands-on experience with containerization and orchestration—essential skills for managing ML models at scale in cloud environments.
Q4
Walk me through your experience with experiment tracking and model registry tools (e.g., MLflow, Weights & Biases). How do you ensure reproducibility and traceability of ML models in production?