MLCommons - Standardize AI Measurement, Enable Informed Decisions

Building trusted, safe, and efficient AI requires better systems for measurement and accountability. MLCommons’ collective engineering with industry and academia continually measures and improves the accuracy, safety, speed, and efficiency of AI technologies – so the market can make informed decisions.
MLCommons is supported by over 125 members and affiliates, including startups, leading companies, academics, and non-profits from around the globe.
At MLCommons, we build and operate open, industry-standard benchmarks and data tooling to measure quality, performance, and risk – so the market can make confident, informed decisions.
Better measurement de-risks and grows the AI market. Benchmarks help balance AI’s benefits and risks with quantitative tools that guide responsible AI development. They provide neutral, consistent measurements of accuracy, speed, and efficiency that help engineers design reliable products and services and help researchers gain new insights to drive tomorrow’s solutions.
The MLCommons AI Risk & Reliability working group is composed of a global consortium of AI industry leaders, practitioners, researchers, and civil society experts committed to building a harmonized approach for safer AI.
Evaluating and delivering more reliable AI systems depends on rigorous, standardized test datasets, and data standards. MLCommons builds open, large-scale, diverse datasets, and a rich ecosystem of techniques and tools for AI data. Our work includes Croissant, today’s metadata standard that makes ML work easier to reproduce and replicate.
Our shared research infrastructure and diverse community aid the scientific research community to derive new insights for new breakthroughs in AI.
We’re a collective of data nerds, AI experts, and enthusiasts who are passionate about accelerating AI. While data, modeling and all that good stuff is critical, it’s the people behind it all that are the bedrock of MLCommons.
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