𝗧𝗵𝗲 𝗨𝗹𝗧𝗶𝗺𝗮𝗧𝗲 𝗚𝗨𝗜𝗗𝗘 𝗧𝗢 𝗗𝗔𝗧𝗔 𝗦𝗖𝗜𝗘𝗡𝗖𝗘 𝗘𝗫𝗣𝗘𝗥𝗜𝗠𝗘𝗡𝗧𝗦 You work on data science projects with notebooks, scripts, and pipelines. Your team needs a solid strategy to track ideas, share results, and revert to solid baselines.
Here's a practical guide to a branching model tailored for data science workflows:
- Prevent experiment sprawl from breaking main research progress
- Keep data, code, and results reproducible across machines and environments
- Separate exploratory work from production-ready code and datasets
- Make it easy to compare experiments and roll back when needed
- Integrate with CI/CD pipelines for automated checks on baseline experiments
Key concepts:
- Baseline branch: a stable reference containing the most recent publishable results
- Feature/experiment branches: isolated work to test ideas
- Data/version control: treat large data with pointers rather than duplicating files
To get started:
- Define a baseline and create a data-refs manifest
- Create an experiments/NAME branch from dev
- Implement a focused hypothesis with bounded changes
- Run a deterministic, small-scale test and record results