Machine Learning Operations (MLOps)

Machine Learning Operations, or MLOps, applies software engineering and DevOps practices to the machine learning lifecycle. It covers reproducible training, model versioning, automated testing, deployment, monitoring, rollback, and retraining so models can operate reliably in production. A mature MLOps process also tracks data and model drift, ownership, evaluation results, and operational dependencies, helping teams move from experiments to maintainable production systems.