Chapter 8: MLOps — from notebook to production

Training the model is maybe 20% of the work. The rest: reproducible experiments, data versioning, containers, deployment, monitoring. A model that shines in a notebook but mysteriously fails in production is the classic MLOps failure mode. This chapter walks the full path—from experiment tracking with MLflow to monitoring data drift in production.

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