DVC

DVC (Data Version Control) is an open-source tool for managing machine learning projects, datasets, and experiments. It provides version control for data and models similar to Git for code, enabling reproducibility and collaboration in ML workflows. DVC integrates with cloud storage and ML frameworks to track changes, share results, and automate pipelines.

Also known as: Data Version Control, DVC Tool, DVC ML, DVC Data, DVC Pipelines

Why learn DVC?

Developers should learn DVC when working on machine learning projects that require reproducible experiments, efficient data management, and team collaboration. It is particularly useful for tracking large datasets, comparing model versions, and automating ML pipelines in production environments, such as in data science teams or AI research labs.

Learning Resources

• docs: DVC Documentation
• tutorial: DVC Tutorial: Getting Started
• course: DVC Course on Coursera
• video: DVC YouTube Channel
• book: Hands-On Data Science with DVC Book