Data Science Teamwork with Git

Promoting ML projects from “proof of concept” to “ongoing project” status reveals how difficult it is to work on them as a team. Researchers and engineers try various model types, feature engineering, and data sources in parallel. Recording, reviewing, and integrating what everyone is working on is difficult and error-prone. The problems worsen as team size increases and teams become distributed.

In this session, Dean shares his experiences of working together on data science projects, the existing challenges , and how to solve these issues using DagsHub, Git and other open-source tools and formats.