A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

🐢 Open-Source Evaluation & Testing library for LLM Agents

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.

A Python package to assess and improve fairness of machine learning models.

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

RAG Time: A 5-week Learning Journey to Mastering RAG

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

FIBO is a SOTA, first open-source, JSON-native text-to-image model built for controllable, predictable, and legally safe image generation.

LangFair is a Python library for conducting use-case level LLM bias and fairness assessments

Open-source testing platform & SDK for LLM and agentic applications. Define what your app should and shouldn't do in plain language, and Rhesis generates hundreds of test scenarios, runs them, and shows you where it breaks before production. Built for cross-functional teams to collaborate.

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.

Carefully curated list of awesome data science resources.

[NeurIPS 2023] Sentry-Image: Detect Any AI-generated Images

Reading list for adversarial perspective and robustness in deep reinforcement learning.

Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

A curated list of awesome academic research, books, code of ethics, courses, databases, data sets, frameworks, institutes, maturity models, newsletters, principles, podcasts, regulations, reports, responsible scale policies, tools and standards related to Responsible, Trustworthy, and Human-Centered AI.

This is an open-source tool to assess and improve the trustworthiness of AI systems.