DVC

DVC (Data Version Control) brings software engineering best practices, specifically Git-like version control, to data, AI/ML, and data science teams. It allows users to manage data and models in the same way they manage code, enabling reproducibility, collaboration, and traceability in data-intensive projects. DVC is designed to serve both individual data scientists and enterprise AI/data engineering teams. For individual data scientists, it offers an easy-to-use Git extension for small data science projects, integrating data version control into workflows with minimal overhead. For enterprise teams, DVC provides a highly scalable data version control infrastructure suitable for complex AI operations and big data environments, including petabyte-scale multimodal object stores and data lakes. By applying version control to data, DVC helps teams track changes, revert to previous versions, and ensure consistency across different stages of their machine learning pipelines. It aims to make data science projects more robust, collaborative, and easier to manage, similar to how Git revolutionized software development.