MLOps

MLOps (Machine Learning Operations) is the set of practices applying DevOps principles to the lifecycle of machine-learning models: versioning code, data and models; CI/CD for training and deployment; monitoring quality and drift; reproducibility of experiments.

It responds to a sobering reality: most ML models built never make it to production, and those that do drift silently as the data evolves.

Reference tools include MLflow, Kubeflow, Vertex AI, SageMaker, Weights & Biases, Metaflow and DVC for data versioning. MLOps precedes and complements LLMOps.