Interview Question
Design a production ML platform that can safely promote models from training to serving.
What a Strong Answer Should Cover
- Trace data and model artifacts
- Automate evaluation gates
- Use a model registry
- Monitor production behavior
- Support rollback
Common Mistakes
- Jumping to a technology before clarifying the problem
- Explaining the solution without the reasoning or trade-offs
- Omitting verification, failure handling, or prevention
Interviewer Follow-ups
- What makes a model eligible for promotion?
- How would you detect a bad model after release?
- How would you roll back safely?
What the interviewer is testing
- Requirements and constraints
- Architecture and trade-offs
- Reliability, security, cost, and operations
Real-World Sample Answer
A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.