Branching Out: 4 Git Workflows for Collaborating on ML
Briefly

The author reflects on their early struggles with R script management and the importance of version control in development. They emphasize that robust systems, such as Git, are vital for tracking changes and ensuring reproducibility. This is especially true in Agile environments where collaboration is key. Though Git allows flexibility, it can lead to chaos if not used properly, making defined workflows necessary for maximizing efficiency. The author encourages those new to Git to familiarize themselves with its principles before implementing team workflows.
It's been more than 15 years since I finished my master's degree, but I'm still haunted by the hair-pulling frustration of managing my R scripts.
Most development, especially with Agile ways of working, thrives on robust version control systems. The ability to track changes, revert to previous versions, and ensure reproducibility within a collaborative codebase can't be an afterthought.
Git is incredibly flexible as a version control system, and it allows developers a lot of freedom in how they manage their code.
Establishing Git workflows can guide your team's development so you're using Git more consistently and efficiently.
Read at towardsdatascience.com
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