Version control is a crucial practice! Without it, your project can become disorganized, making it difficult to get back to the desired point. You risk losing critical model configurations, weights, experiment results from extensive training periods, and even the entire project. You may also find yourself in disagreements and conflicts with your teammates when code fails, hindering effective collaboration. In this article, we explore the importance of version control through a practical example that employs some of the most common tools in the field. The full code base for this article can be accessed at the associated repository.
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. Versioning of the model
· Conclusion
Version control is the practice of recording changes to a file or file configuration over time, using version control systems, so that we can recover specific versions later. In MLOps, version control is one of the fundamental principles that I consider the first to consider when starting your machine learning projects. To ensure we reap the full benefits, version control should be applied at different steps of the machine learning workflow, including datahe Machine learning model (ML model), and code.
Why version? Using version control for code, data, and models allows reproducibility (which is another important principle of MLOps) by allowing specific states of the project to be recreated at any given time; follow-up and supervision changes by establishing a systematic approach to capture, document and manage changes throughout the development life cycle…
