Version Control for Data and Models: DVC and Git

Git is great for code, but what about your 50GB dataset? Or the 100 different model versions you trained? I learned the hard way when I accidentally overwrote a model that had been training for three days. That’s when I started using DVC (Data Version Control). It’s not just for code anymore; you need to version your data and your artifacts. Reproducibility is key in ML. If you can’t go back to the exact dataset and code that produced a specific result, you’re going to have a bad time.