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Interview SkillsMLOps

What is MLOps? What is data drift? What is a model registry? How do you monitor a model in production? What is CI/CD for ML? How does a feature store work in production ML? What is concept drift and how do you handle it? What metrics evaluate document extraction accuracy? How should 98% extraction accuracy be measured? BLEU, ROUGE, BERTScore — which is best for LLM output evaluation? How do you evaluate hallucination in an LLM extraction pipeline? What is LLM-as-a-judge? What are its risks? How do you build an LLM evaluation component? Offline vs online evaluation for LLM systems? What is a regression test suite for LLM apps? How do you prevent regressions? How do you validate automation and accuracy in document processing? How do you A/B test prompts in a production system?

MLOps Interview Questions

Production ML lifecycle concepts for deployment, monitoring, and reliability interviews.

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