#scikit-learn

End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.

Bridge your scikit-learn scaler parameters between Python sessions and users. Distribute your scaling across multiple processes and data subsets.

MLeap: Deploy ML Pipelines to Production

Scaleout Edge: Sovereign Edge AI orchestration and Federated Learning

Impress your boss with interactive Decision Tree visualization

High-performance interpretable ML classifier using High Utility Gain patterns (IEEE Access 2024). C++ accelerated, scikit-learn compatible, with EBM-style explanations, adaptive binning, pattern pruning, and deployment tooling for regulated domains.