A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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Updated
Feb 5, 2024
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
🐢 The testing framework for ML models, from tabular to LLMs
A Python package to assess and improve fairness of machine learning models.
moDel Agnostic Language for Exploration and eXplanation
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.
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
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
Référentiel d'évaluation data science responsable et de confiance
Reading list for adversarial perspective and robustness in deep reinforcement learning.
[NeurIPS 2023] Sentry-Image: Detect Any AI-generated Images
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
Python library for implementing Responsible AI mitigations.
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.
Official code of "StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis" (CVPR 2022)
[ICCV 2023 Oral, Best Paper Finalist] ITI-GEN: Inclusive Text-to-Image Generation
Carefully curated list of awesome data science resources.
Oracle Guardian AI Open Source Project is a library consisting of tools to assess fairness/bias and privacy of machine learning models and data sets.
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