Mastering Model Versioning with DVC and Git: Lessons from the Trenches
When I started working with DVC, I quickly realized there was a gap between theory and what actually happens in practice. This post is about my experience with model versioning using dvc and git. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Model Versioning As I delved into the world of machine learning operations (MLOps), I quickly realized the importance of model versioning. Keeping track of changes to models, datasets, and training pipelines is crucial for reproducibility and collaboration. In this article, I'll share my experience with using DVC (Data Version Control) and Git for model versioning, highlighting the lessons I learned, the mistakes I made, and the best practices I discovered. What is DVC and How Does it Work? DVC is a tool that helps track large files, such as datasets and model artifacts, outside of Git. This is essential becaus...