What is MLOps?

MLOps (Machine Learning Operations) is the practice of applying DevOps principles to the ML lifecycle to deploy and maintain ML models reliably and efficiently. It covers: experiment tracking (MLflow, Weights & Biases), data versioning (DVC), model versioning, CI/CD for ML (automated retraining pipelines), model serving (TorchServe, TF Serving, Triton), monitoring (detecting data drift, model degradation), and feature stores (Feast). MLOps bridges the gap between ML research and production deployment, addressing challenges like training-serving skew and concept drift.