In the world of machine learning, managing data, code, and models efficiently is crucial for ensuring reproducibility and collaboration. If you’re working on machine learning or data science projects, you’ve likely struggled with managing large datasets, models, and experiment results.
While Git is great for tracking code, it wasn’t built to handle large files or complex workflows. This is where DVC (Data Version Control) shines – helping you track datasets, models, and experiments alongside your code, making your projects scalable and reproducible.
Continue reading “A Simple Guide to DVC: What It Is and How to Get Started”