MLOps
The engineering discipline of deploying, monitoring, and maintaining ML systems in production
MLOps
Training a model is the easy part. The hard part is everything around it: tracking experiments so you can reproduce last Tuesday's best run, versioning models so promotion to production is a rollback-safe operation, building pipelines that retrain without babysitting, and knowing whether your model is actually winning in production. This section covers the infrastructure and practices that separate a notebook demo from a reliable ML system.