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Model Deployment Q&A
1What is model deployment?
Answer: Integrating trained model into production systems for real predictions.
2Batch vs real-time inference?
Answer: Batch scores in chunks; real-time predicts per request.
3What is model serving?
Answer: Exposing model via API endpoint/service.
4Why containerization?
Answer: Consistent runtime across environments.
5Blue-green deployment?
Answer: Switch traffic from old to new version with rollback safety.
6What is canary release?
Answer: Roll out to small user subset before full rollout.
7How monitor deployed models?
Answer: Track latency, errors, drift, and business KPIs.
8What is model versioning?
Answer: Tracking model artifacts, configs, and lineage for reproducibility.
9Common deployment risk?
Answer: Training-serving skew between offline and online features.
10One-line summary?
Answer: Deployment turns ML prototypes into measurable business value.