To simplify workflows in ML systems, it’s important to know how to collaborate, document, and benchmark experiments — even more so in a collaborative environment.
The full guide is hosted as a GitHub Pages site at MLForResearchScientists. It was also published in Editor’s Picks on Towards Data Science.
The guide covers standard open-source and free tools for setting up reproducible ML research pipelines, version control practices, experiment tracking, and collaboration patterns.
See the full guide at the GitHub Pages link above.