ml-tooling

An interpretable battery health engine that detects hidden points of no return instead of just predicting health %. It models stress, buffer, and degradation intensity, discovers Stable/Drifting/Irreversible regimes via GMM, and learns simple Decision Tree thresholds, with a Streamlit app for diagnostics and what-if scenarios.

A Python library for building local web apps to manually classify images into custom categories - perfect for preparing ML training datasets.

Time-travel debugging for machine learning predictions by logging and replaying decisions with their original model version and execution context.

Cross-vendor edge-AI deployment diagnostician: explains in plain language why your model breaks or slows down on real edge hardware, grounded in your actual conversion logs and profiler artifacts. Work in progress.

Hugging Face hub yardımcıları — repo yedekleme ve LoRA eğitim setup scriptleri