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

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.