Fairness in Machine Learning Benchmarks

Today we’re joined by Peter Mattson, General Chair at MLPerf, a Staff Engineer at Google, and President of MLCommons. 

In our conversation with Peter, we discuss MLCommons and MLPerf, the former an open engineering group with the goal of accelerating machine learning innovation, and the latter a set of standardized Machine Learning speed benchmarks used to measure things like model training speed, throughput speed for inference. 

We explore the target user for the MLPerf benchmarks, the need for benchmarks in the ethics, bias, fairness space, and how they’re approaching this through the "People’s Speech" datasets. We also walk through the MLCommons best practices of getting a model into production, why it's so difficult, and how MLCube can make the process easier for researchers and developers.

The complete show notes page for this episode can be found at twimlai.com/go/434.