Automatic generation of LLM guardrails with NeMo and Giskard

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Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.It is still a beta and I would love to hear your feedback if you have the time to try it out.We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.If you are interested in the code:https://github.com/Giskard-AI/giskard/tree/main/python

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Website: docs.giskard.ai/en/latest/integrations/nemoguardrails/nemoguardrails-integration.html