MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production. It covers the full lifecycle: data pipelines, model training, versioning, deployment, monitoring for drift, and retraining. MLOps ensures models don't degrade silently after deployment — a critical concern as AI becomes embedded in business-critical workflows.