Accelerating ML innovation at MLCommons
Practical AI: Machine Learning, Data Science
English - January 19, 2021 15:30 - 51 minutes - 47 MB - ★★★★★ - 37 ratingsTechnology Education How To changelog machine learning deep learning artificial intelligence neural networks computer vision Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technology for the public good. David Kanter, the executive director of MLCommons, joins us to discuss the launch and the ambitions of the organization. In particular we discuss the three pillars of the organization: Benchmarks and Metrics (e.g. MLPerf), Datasets and Models (e.g. People’s Speech), and Best Practices (e.g. MLCube).
MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technology for the public good. David Kanter, the executive director of MLCommons, joins us to discuss the launch and the ambitions of the organization.
In particular we discuss the three pillars of the organization: Benchmarks and Metrics (e.g. MLPerf), Datasets and Models (e.g. People’s Speech), and Best Practices (e.g. MLCube).
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Featuring:
David Kanter – Twitter, GitHubChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website
Show Notes:
Learn more about People’s Speech
Get involved with the People’s Speech project
MLCube GitHub, including several different examples
MLCube Mailing list
MLPerf Training Benchmarks
MLPerf Training HPC Benchmarks
MLPerf Inference Datacenter Benchmarks
MLPerf Inference Edge Benchmarks
MLPerf Inference Mobile Benchmarks
MLCommons on Twitter
Something missing or broken? PRs welcome!