In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.

We had a great conversation spanning many topics, including:

His personal blog, which contains some of the best explainers on emerging topics in data management and distributed systems.
The role of machine learning in operational analytics and business intelligence.
Machine learning benchmarks—specifically two recent ML initiatives that he’s been involved with: DAWNBench and MLPerf.
Trends in data management and in tools for machine learning development, governance, and operations.

Related resources:

“Setting benchmarks in machine learning”: Dave Patterson, Peter Bailis, and other industry leaders discuss how MLPerf will define an entire suite of benchmarks to measure performance of software, hardware, and cloud systems.
“The quest for high-quality data”
“RISELab’s AutoPandas hints at automation tech that will change the nature of software development”
Jeff Jonas on “Real-time entity resolution made accessible”
“What are model governance and model operations?”
“We need to build machine learning tools to augment machine learning engineers”

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