DVC
UnclaimedManage data and machine learning models like code with Git-like version control.
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TL;DR - DVC
- Applies Git-like version control to data and machine learning models.
- Enables reproducibility, collaboration, and traceability for data science projects.
- Scalable for both individual data scientists and enterprise AI teams.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Free and open source
- Brings software engineering best practices to data science
- Enhances reproducibility and collaboration
- Scalable for various project sizes
- Integrates well with existing Git workflows
Cons
- Requires familiarity with Git concepts
- May have a learning curve for new users
Key Features
Git-like data version controlIntegration with GitSupport for large datasets and modelsScalable for enterprise AI operationsCompatible with object stores and data lakesVS Code extension available
Pricing Plans
lakeFS Enterprise
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- Highly scalable data version control infrastructure
- Designed for complex AI operations and big data environments
- Petabyte-scale multimodal object stores and data lakes
lakeFS (Free and open source)
Free
DVC (Free and open source)
Free
- Easy to use data version control Git extension
- For small data science projects
- Apply data version control to your data science workflows with minimal overhead
About DVC
By Toolradar Team·
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.
How we evaluate tools →Source: dvc.org
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DVC FAQ
What is DVC?
DVC (Data Version Control) is an open-source tool that extends Git's version control capabilities to data and machine learning models. It helps data scientists and AI/ML teams manage, track, and reproduce their data-intensive projects.
How much does DVC cost?
DVC is free and open source.
Is DVC free?
Yes, DVC is free and open source.
Who is DVC for?
DVC is for individual data scientists working on small projects, as well as enterprise AI and data engineering teams managing complex AI operations and big data environments with petabyte-scale object stores and data lakes.
Source: dvc.org