PyPLTool : Runtime trust layer for machine learning systems. Detects drift, uncertainty, and reliability risks in production ML models.

ByteStack Labs marketplace for Claude Code. Open reliability skills that audit AI which passes evaluation but fails in production. Every number reproducible.

TrainKeeper is a minimal-decision, high-signal toolkit for building reproducible, debuggable, and efficient ML training systems. It adds guardrails inside training loops without replacing your existing stack.

This project is a production-grade autonomous control system designed to maintain machine learning model integrity through a closed-loop Detect → Diagnose → Decide → Act → Explain cycle. Unlike traditional monitoring that requires slow human intervention, SHMLP autonomously identifies data drift, concept shift, and inference anomalies to execute