I just finished building an MLOps project that I'd like to share with the community. The goal was to create a production‑ready pipeline that covers the entire ML lifecycle. GitHub: https://github.com/avinashmnth2507-dev/mlops-house-price-predictor.git Features: ✅ Data versioning (DVC) ✅ Experiment tracking (MLflow) ✅ Hyperparameter tuning (Optuna) ✅ Model serving (FastAPI) ✅ Containerization (Docker) ✅ CI/CD (GitHub Actions → GHCR) ✅ Kubernetes deployment (Minikube) ✅ Drift monitoring (PSI) ✅ Monitoring stack (Prometheus + Grafana) ✅ FinOps cost tracking Known issue: The Grafana dashboard is currently showing "No data" due to a Prometheus scrape issue in my local Minikube cluster. I'm actively debugging – suggestions welcome. I'm especially interested in feedback on: The GitHub Actions workflow Kubernetes manifests (deployment.yaml, service.yaml) The drift monitoring implementation Open to all constructive criticism. Thanks for looking!