model-debugging

Monitoring platform for ML teams building real‑world AI at scale.

XAI-driven augmentation & diagnostics for PyTorch vision - find model failures, fix with saliency-guided augmentation (ICD/AICD), prove with auditable reports.

Custom ML tracking experiment and debugging tools.

Autonomous failure investigation, root-cause analysis, and validated interventions for LLMs, VLMs, and agents.

ML is a tool for diagnosing model errors: segments, fairness, and calibration

PyTorch NaNs are silent killers. This hook catches them at the exact layer and batch — with ~3 ms overhead vs ~7 ms for set_detect_anomaly.

Neural network visual debugger for model graph inspection, activation health, and gradient diagnostics.

Behavioral debugging for neural networks: trace checkpoint changes to representations, mechanisms, and influential training data.

Easy-to-use UI based tool that visualizes the internal layers and activations of any Pytorch network that takes image as input , built using PyQt

Mechanistic interpretability that ships: MAIR-backed evidence bundles, receipts, and comparison packets.

Stop guessing why your MLX model outputs garbage. Triage in 30 seconds — no model load required.

ML regression diagnosis: matched Banking77 localization studies, competing repairs and explicit ambiguity when evidence cannot identify a unique cause.