Women Who Code DS Talk: Feature Engineering with Hamilton

Women Who Code Data Science talk on Feature Engineering with Hamilton.
[Posted with Permission from WWC:DS.]
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 Women Who Code:
Women Who Code is the largest and most active community of technical women in the world. Our mission is to inspire diverse women to excel in technology careers. Join our community by visiting womenwhocode.com and sign up to become a member.
beacons.ai/wwcodedatascience
Hamilton links:
github.com/DAGWorks-Inc/hamilton
hamilton.dagworks.io/en/latest/
www.tryhamilton.dev/
blog.dagworks.io/ (blogs on feature engineering)