Name: The Hamilton Framework for Feature Engineering
Start: 2025-05-16T04:00:01-0500
End: 2025-05-16T04:00:01-0500

Friday May 16, 2025 TBA

Deploying ML models in production while at the same time preserving the ability to tweak and iterate on the features is a tricky task. Often models are developed and trained by data scientists in notebooks, then handed over to machine learning engineers who productionalize the code, and then supported by software engineers as they run in production. This results in slower iteration speed as any tweak to the model needs to involve several people and tweaks to several codebases, and also introduces potential for errors since the code is copied from notebooks and then edited before it gets to production. In this talk I will describe how we address these and some other issues using the Hamilton micro-orchestration framework for feature engineering.

Speakers

Michael Chmutov, PhD

Manager Data Science, C.H. Robinson

Michael started out as an academic mathematician, getting his Ph.D. from University of Michigan. After a postdoc at UMN, he moved to industry, and has been with C. H. Robinson since 2018. He is a lead on the Matching Team, recommending loads to carriers.

Friday May 16, 2025 TBA

4 - Mostly Technical

Data Tech 2025

Michael Chmutov, PhD

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