Building the ML ecosystem
Abstract
Machine Learning has tremendous potential, but it is an industry in its infancy. The MLCommons Association is a non-profit that seeks to accelerate the development of ML by bringing together researchers and engineers to build the ML ecosystem. The MLCommons Association works to develop benchmarks, open data, and best practices. MLCommons has over a dozen active working groups, and has produced the industry standard MLPerf benchmarks, the People's Speech and Multilingual Spoken Words Corpus datasets (announced this year at NeuRIPs), and the MLCube best practice for model portability. This talk will provide a broad overview of the MLCommons Association efforts, with a particular emphasis on open problems that may be of interest to ETH Zurich researchers and the DataPerf "benchmark suite for data" developed in collaboration with researchers at ETH.
Short Bio
Peter Mattson is a staff engineer at Google. He co-founded and is President of MLCommons, and co-founded and was General Chair of the MLPerf consortium that preceded it. Previously, he founded the Programming Systems and Applications Group at NVIDIA Research, was VP of software infrastructure for Stream Processors Inc (SPI), and was a managing engineer at Reservoir Labs. His research focuses on understanding machine learning models and data through quantitative metrics and analysis. Peter holds a PhD and MS from Stanford University and a BS from the University of Washington.