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DVC
FREE · FreeDVC (Data Version Control) is an open-source version control system for data and ML models, tracking datasets and experiments.
4.4(81 reviews) 0 views Updated 8/27/2026
Overview
DVC is a Git-based version control system for data science and ML projects. It tracks datasets, models, and experiments, enabling reproducible ML pipelines and collaboration.
Key Features
Open-source version control system for data and ML modelsGit-compatible CLI that extends versioning to large files and datasetsPipeline definition with dependency tracking for reproducible ML workflowsRemote storage support for S3, GCS, Azure, and SSH locationsExperiment tracking with parameter and metric logging for comparison
Pros
Solves the 'how do I version a 50GB dataset?' problem elegantlyPipeline system makes ML workflows reproducible like MakefilesWorks with existing Git workflows — no new SCM to learn
Cons
CLI-only interface has a learning curve for non-CLI usersRemote storage setup requires some DevOps knowledge
Pricing
DVC pricing starts at Free. Enterprise pricing available.
Conclusion
DVC is a good choice for Data version control for ML. Pricing is competitive.
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4.4
81 reviews