Graphsignal – Production Model Monitoring: Anomalies, Drift, Exceptions
In plain words
Graphsignal is a production model monitoring tool that tracks anomalies, drift, and exceptions in deployed machine learning models. It helps data scientists and ML engineers identify when models behave unexpectedly or degrade over time. The tool integrates with production environments to provide visibility into model performance, making it useful for teams that need to maintain reliable machine learning systems in live applications.
written from the facts on this page · September 2026
Does the same job
all alternatives →- GMGraphsignal – Machine learning profiler for training and inference2022 · graphsignal.com · ▲35
Hi HN, I'm the founder of Graphsignal (https://graphsignal.com). Graphsignal is a machine learning profiler. We've created it to make ML profiling simple and usable. It provides performance summaries, ML operation and kernel level statistics as well as detailed resource usage information necessary for making training and inference faster and more efficient. Profilers help fix performance issues, improve user experience and reduce computation costs. Such improvements benefit machine learning profoundly; model training jobs that run for hours or days could be made much shorter and…
- GMGraphsignal – ML profiler to speed up training and inference2022 · github.com · ▲16
Hi, Graphsignal founder here. We've launched Graphsignal earlier this year to make machine learning profiling practical and easy to use. Basically, it enables the profile-optimize-benchmark loop. For example, making inference faster by optimizing an ML model, while still maintaining accuracy. We've make a lot of progress that I wanted to share. The profiler now natively supports TensorFlow, Keras, PyTorch, PyTorch Lightning, Hugging Face, XGBoost and JAX frameworks along with built-in support for distributed workloads. Profiles now include tracing information in chrome trace format. Process…
- FGForge – Guardrails take an 8B model from 53% to 99% on agentic tasksMay 2026 · github.com · ▲687
Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…
Human Behavior24d ago · humanbehavior.co · ▲273Product analytics told you what happened. We handle it.
- FRFast Real-Time Anomaly Detection in Dynamic Graphs2020 · github.com · ▲118
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