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.

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