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 vital practice! Without it, your project may turn out to be disorganized, making it difficult to roll back to any desired point. You risk losing critical model configurations, weights, experiment results from extensive training periods, and even the whole project itself. You may additionally end up in disagreements and conflicts together with your teammates when the code breaks, hindering effective collaboration. In this text, we navigate the importance of version control through a practical example that employs a number of the most typical tools in the sphere. All the codebase for this text is accessible in the associated repository.

Table of contents:

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

Version controlling is the practice of recording changes to a file or setting of files over time, using version control systems, in order that we are able to recall specific versions later. In MLOps, version controlling is one among the principal principles that I consider it as the primary one to think about when starting your machine learning projects. To make sure that we harness all the advantages, version control needs to be applied across different machine learning workflow steps, including data, the Machine Learning model (ML model), and code.

Why versioning? Using version control for code, data, and models enables reproducibility (which is an one other necessary MLOps principle) by allowing to recreate specific states of the project at any given cut-off date; tracking and monitoring changes by establishing a scientific approach to capturing, documenting, and managing changes throughout the event lifecycle…


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