Episode 17: Michael Munn, Google: Machine Learning Design Patterns

Inference #17: ML Design Patterns with Michael Munn from Google Information can be lost in translation. As new technologies require a unified framework for discussion, they too require it for semantics. Michael Munn, along with his co-authors Valliappa Lakshmanan and Sara Robinson, released a book called ML Design Patterns to help codify common modeling- and engineering problems, solutions, and approaches into uniform language, aiming to democratize ML comprehension. Michael is a professor and mathematician by background. At Google, he works with Google Cloud Platform’s customer-facing projects and is one of the driving forces behind Google’s Advanced Solutions Lab. Tune in to learn about the difference between academic vs. democratic ML, best practice sharing between Google’s teams and matching of Google’s MLOps principles to customer ways of working, and upcoming MLOps trends. https://youtu.be/hNtJJ5R_T9s

Om Podcasten

In a world in which everyone is talking about AI, a real track record is hard-earned. Inference dives deep into data driven organizations and their machine learning operations to understand what it means to work with AI. Our guests range from lead data scientists to founders, strategists and engineers. Learn more about what production ready AI means today with Inference by Silo AI. Your host is Ville Hulkko, Co-founder of Silo AI.