# MLOps MLOps applies [[DevOps]] principles to machine-learning systems: version-controlled code, automated training pipelines, model registries, CI/CD for models, monitoring in production, and rollback strategies. The point is to make the path from "model trained in a notebook" to "model serving live traffic, reproducibly" boring — not heroic. MLOps differs from DevOps in three ways: the artifact is not just code but **code + data + model**, performance degrades silently as data drifts, and experiments are first-class citizens with their own lifecycle. ## References ## Related - [[MLflow]] - [[Model Registry]] - [[ML Reproducibility]] - [[ML Deployment Patterns]] - [[LLM Monitoring]] - [[DevOps]]