A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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Updated
Nov 26, 2024
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
🐢 Open-Source Evaluation & Testing for ML & LLM systems
A Python package to assess and improve fairness of machine learning models.
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
moDel Agnostic Language for Exploration and eXplanation
Deliver safe & effective language models
A toolkit that streamlines and automates the generation of model cards
💡 Adversarial attacks on explanations and how to defend them
Carefully curated list of awesome data science resources.
A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.
[NeurIPS 2023] Sentry-Image: Detect Any AI-generated Images
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
Reading list for adversarial perspective and robustness in deep reinforcement learning.
This is an open-source tool to assess and improve the trustworthiness of AI systems.
Référentiel d'évaluation data science responsable et de confiance
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments
[ICCV 2023 Oral, Best Paper Finalist] ITI-GEN: Inclusive Text-to-Image Generation
A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.
PyTorch package to train and audit ML models for Individual Fairness
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