Version Controlling in Practice: Data, ML Model, and Code

A Step-by-Step Guide to Versioning in MLOps

Photo by Christopher Gower on Unsplash

Version control is a crucial practice! Without it, your project may become disorganized, making it challenging to roll back to any desired point. You risk losing critical model configurations, weights, experiment results from extensive training periods, and even the entire project itself. You might also find yourself in disagreements and conflicts with your teammates when the code breaks, hindering effective collaboration. In this article, we navigate the importance of version control through a practical example that employs some of the most common tools in the field. The entire codebase for this article is accessible in the associated repository.

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Table of contents:

· 1. Introduction
· 2. Tools
· 3. Setting up your project
∘ 3.1. Project folder
∘ 3.2. Project environment
· 4. Code versioning
· 5. Data versioning
· 6. Model versioning
· Conclusion

1. Introduction