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!
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