MLOps: Applying DevOps to ML
summary
Describes applying DevOps practices to machine learning by mapping planning, code management, CI/CD and deployment to model workflows; introduces concrete steps like model versioning, automated testing and containerized serving; explains monitoring strategies including data drift monitoring and rollout metrics to measure whether model changes deliver value; contrasts which DevOps steps translate directly and which require adaptation for data pipelines, training jobs and production inference.
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