Q1
Walk me through how you would design a CI/CD pipeline for deploying machine learning models to production, including model versioning, validation gates, and rollback strategies.
Why they ask this:* They want to assess your understanding of production ML workflows, deployment automation, and your ability to design systems that balance speed with safety in model releases.
Q2
Describe your experience with monitoring and observability for ML systems. What metrics would you track for model performance drift, and how would you set up alerts for data or prediction quality degradation?
Why they ask this:* This tests whether you understand the unique challenges of ML systems in production—that models degrade over time—and whether you can implement proactive monitoring beyond standard infrastructure metrics.
Q3
Explain how you would implement infrastructure-as-code (IaC) for managing ML training and serving environments. What tools have you used, and how do you handle configuration across different environments?
Why they ask this:* They're evaluating your ability to create reproducible, scalable, and maintainable ML infrastructure, and whether you follow DevOps best practices in an MLOps context.
Q4
Describe your approach to managing ML experiment tracking and reproducibility. How do you ensure team members can reproduce results, and what tools or frameworks have you integrated into your workflows?