MLCommons

Better Machine Learning
for Everyone


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.

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Accelerating Machine Learning Innovation

In collaboration with our 125+ founding Members and Affiliates, including startups, leading companies, academics, and non-profits from around the globe, we democratize machine learning through open industry-standard benchmarks that measure quality and performance and by building open, large-scale, and diverse datasets to improve AI models.

Benchmarks

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Benchmarks help balance the benefits and risks of AI through quantitative tools that guide effective and responsible AI development. They provide consistent measurements of accuracy, safety, speed, and efficiency which enable engineers to design reliable products and services and help researchers gain new insights to drive the solutions of tomorrow.

Datasets

Evaluating AI systems depends on rigorous, standardized test datasets. MLCommons builds open, large-scale, and diverse datasets and a rich ecosystem of techniques and tools for AI data, helping the broader community deliver more accurate and safer AI systems.

MLCommons Members

MLCommons is supported by our 125+ founding members and affiliates, including startups, leading companies, academics, and non-profits from around the globe.


Founding Members

Members

Ways to Get Involved

MLCommons is a community-driven and community-funded effort. We welcome all corporations, academic researchers, nonprofits, government organizations, and individuals on a non-discriminatory basis. Join us!

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