MLOps (Machine Learning Operations) applies DevOps principles to ML systems, covering deployment, monitoring, and lifecycle management. Core practices: Version control for code/data/models, automated testing, CI/CD for ML, monitoring and observability, reproducibility. MLOps vs DevOps: Adds data versioning, model versioning, experiment tracking, drift detection, feature stores. ML-specific challenges. Lifecycle stages: Development (experiment, train), staging (validate, test), production (deploy, monitor), retraining (continuous improvement). Key components: Experiment tracking: MLflow, W&B, Neptune. Feature stores: Feast, Tecton. Model registry: MLflow, custom solutions. Pipelines: Kubeflow, Airflow, Vertex AI. Serving: TorchServe, Triton, vLLM. Maturity levels: Manual (ad-hoc), ML pipeline automation, CI/CD automation, fully automated MLOps. Challenges: Data quality, model reproducibility, deployment complexity, monitoring drift, team coordination. Organizations: ML teams, platform teams, data teams collaborating. Best practices: Automate everything, version everything, monitor everything, enable reproducibility. Essential for production ML at scale.