Data Version Control – and much more – for AI projects Free and open source, forever. Manage and version images, audio, video, and text files in storage and organize your ML modeling process into a reproducible workflow.

Get a quick introduction to the major features of DVC for data science and machine learning projects: version data, access it anywhere, capture pipelines and metrics, and manage experiments.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

Data Version Control is a free, open-source tool for data management, ML pipeline automation, and experiment management. This helps data science and machine learning teams manage large datasets, make projects reproducible, and collaborate better.

Data Version Control (DVC) lets you capture the versions of your data and models in Git commits, while storing them on-premises or in cloud storage. It also provides a mechanism to switch between these different data contents.

track and save data and machine learning models the same way you capture code; create and switch between versions of data and ML models easily; understand how datasets and ML artifacts were built in the first place; compare model metrics among experiments; adopt engineering tools and best practices in data science projects; DVC is for you!

Get hands-on experience with data versioning in a basic machine learning version control scenario: managing multiple datasets and ML models using DVC.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.