Implements the full ML lifecycle: time-aware feature engineering, XGBoost modeling with Optuna + MLflow, FastAPI inference, Streamlit dashboard, Dockerized services, CI/CD via GitHub Actions, and deployment on AWS ECS Fargate (S3, ECR, ALB).
docker aws machine-learning time-series ci-cd xgboost regression-models model-deployment mlops mlflow github-actions fastapi optuna production-ml streamlit end-to-end-ml house-price-pre
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Updated
Feb 1, 2026 - Jupyter Notebook