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Chapter 3.1 - MLOps & Deployment Questions

Info Comprehensive interview questions about RAG vs Fine-tuning and advanced architectures.

Tier 1: Fundamentals & Strategy​

Q1: Questions here​

Answer: ans here

Q2: Q2​

Answer: Ans2

Architectural & System Design Questions​

  1. How would you deploy an ML model into production?
  2. What is model drift and how do you detect it?
  3. What is the difference between CI/CD and CT pipelines in ML?
  4. What metrics would you monitor in production?
  5. What causes training-serving skew?
  6. How would you perform canary deployments for ML models?
  7. How would you automate model retraining?
  8. How would you monitor data quality in production?
  9. How would you manage feature stores across training and inference?
  10. How would you design rollback and disaster recovery for ML systems?