1. MLOps fundamentals & model lifecycle 2. Data versioning & experiment tracking (MLflow, DVC) 3. Containerization with Docker & Kubernetes orchestration 4. Model deployment & serving (API, cloud, serverless) 5. Monitoring, drift detection & automatic retraining 6. Project