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Feature Engineering with Hamilton

🖥 Presented by Women Who Code Data Science 👩‍💻 Speaker: Stefan Krawczyk ✨ Topic: Feature Engineering with Hamilton Women Who Code Data Science invites you to learn a new open source framework with us! 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. Hamilton, a novel open source Python framework solved their pain points by changing their working paradigm. Hamilton enables a simpler paradigm for Data Science, Machine Learning, & Data Engineering teams to create, maintain, execute, and scale code for feature/data transforms, especially when there is a chain of them. Hamilton does this by building a DAG of dependencies directly from Python functions. What You'll Learn: How feature engineering can lead to messy code - which can slow you and your team down over time -- an underrated problem. Hamilton is an open source library to help organize your feature/data engineering code. Hamilton's benefits help a team remain effective over their code base, no matter how many features the code base has, or who wrote them. E.g. unit testing, documentation, data quality, tagging, etc. About speaker: Stefan is the author of a popular open source framework called Hamilton and guest lecturer at Stanford’s Machine Learning Systems Design course. He 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. For our 💬 slack channel, 🎥 previous event recordings, 🗓 upcoming events, 💻 GitHub repo and more check us out on https://beacons.ai/wwcodedatascience