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End-to-end ML pipeline for predicting arrests in Chicago crimes dataset (8.5M records). Built with Polars, XGBoost GPU, DuckDB, FastAPI, and Streamlit. Features incremental data ingestion, automated drift detection, and model retraining orchestrated with Modal.
End-to-end ML pipeline for predicting arrests in Chicago crimes dataset (8.5M records). Built with Polars, XGBoost GPU, DuckDB, FastAPI, and Streamlit. Features incremental data ingestion, automated drift detection, and model retraining orchestrated with Modal.
