mlops

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.