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”