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Stefan Krawczyk: Hamilton: Natively bringing SWE best practices to python data transformations

A hands-on leader and Silicon Valley veteran, Stefan has spent over 15 years working across many parts of the stack. For the last decade, he's focused primarily on data and machine learning related systems and their connection to building product applications. He has built many 0 to 1 and 1 to 3 versions of these systems at places like Stanford, Honda Research, LinkedIn, Nextdoor, Idibon, and Stitch Fix.

A regular conference speaker, Stefan has guest lectured at Stanford’s Machine Learning Systems Design course and is an author of a popular open source framework called Hamilton.

Stefan is currently CEO and co-founder of DAGWorks.

Hamilton: Natively bringing SWE best practices to python data transformations

At Stitch Fix, a data science team’s feature generation process was causing them iteration & operational frustrations in delivering time-series forecasts for the business. It wasn’t the scale of data that was the problem, it was their code. In this talk I’ll present Hamilton, a novel open source Python framework that solved their pain points by changing their working paradigm.

Specifically, Hamilton enables a simpler and more productive approach for data science & data engineering teams to create, maintain, execute, and scale both the code (human) and computational sides of feature/data transforms. In this talk I’ll cover the motivation & backstory, what the Hamilton paradigm is and how it works, and how it naturally guides you into doing software best practices without really thinking about it.

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