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MLOps Q&A
1What is MLOps?
Answer: Practices for reliable, repeatable, and scalable ML lifecycle management.
2Why MLOps needed?
Answer: Bridges gap between experimentation and production reliability.
3CI/CD in ML?
Answer: Automated testing, packaging, and deployment for code/data/models.
4What is feature store?
Answer: Centralized reusable feature management for training and serving.
5Model registry role?
Answer: Versioning and governance of model artifacts and stages.
6What is drift?
Answer: Data or concept shift causing model performance degradation.
7How handle drift?
Answer: Monitor, alert, retrain, and redeploy based on policies.
8What is reproducibility in ML?
Answer: Ability to recreate results with same data/code/config.
9Common MLOps tools?
Answer: MLflow, Kubeflow, Airflow, DVC, Docker, Kubernetes.
10One-line summary?
Answer: MLOps operationalizes ML systems with software engineering discipline.