01End-to-end ML pipeline orchestration using Kubeflow and Airflow
02Feature store implementation for consistent training and serving data
03Production-grade model serving with FastAPI and Kubernetes HPA configurations
04Automated data and concept drift detection using statistical testing
0550 GitHub stars
06Centralized model registry management and experiment tracking with MLflow