How would you implement CI/CD for machine learning pipelines? What testing strategies would you use, and how does ML CI/CD differ from traditional software CI/CD?
formulate your answer, then —
tldr
ML CI/CD extends software CI/CD by treating data and model artifacts as first-class citizens under test. Layer your tests: unit tests for transforms → data validation → training sanity checks → evaluation gate vs. production baseline → shadow deployment. The evaluation gate (does the new model beat the old one?) is the critical step that software CI/CD has no equivalent for.
follow-up
- How would you version control datasets? What's the difference between DVC's approach and just storing data in S3 with timestamped paths?
- What does a good model card look like, and why is it important in an MLOps context?
- How would you handle the case where a new model passes all offline evaluation gates but then underperforms in production A/B testing?