Implement Production-Grade Algorithms Without Scikit-Learn. Master the low-level implementation of machine learning algorithms for production environments where performance, thread-safety, and numerical stability are paramount.
Vectorized Foundations with Polars & NumPy - Architect high-performance data pipelines using the Polars expression API Classical ML Algorithms from Scratch - Implement core ML algorithms using only NumPy and custom extensions The Gradient Boosting Engine from Scratch - Implement the XGBoost/LightGBM logic using only NumPy and custom C++ extensions Production Deployment & MLOps - Build thread-safe, scikit-learn compatible estimators from scratch
The C++ ML Server.
