DVC (Data Version Control)
DVC is an open-source data version control system that enables data scientists and ML engineers to manage data the same way code is managed with Git. By applying a Git-like model, DVC brings software engineering best practices to data, AI/ML, and data science teams.
Two Product Offerings
DVC now offers two distinct solutions under its umbrella:
1. DVC Git Extension (For Local Workflows)
The original DVC tool designed for individual data scientists working on small to medium data science projects. It operates as a Git extension, allowing version control of datasets, ML models, and experiments directly within Git workflows with minimal overhead.
2. lakeFS (For AI/ML & Data Infrastructure)
A highly scalable data version control infrastructure designed for enterprise AI and data engineering teams. It handles complex AI operations and big data environments with petabyte-scale multimodal object stores and data lakes.
Community & Ecosystem
DVC has been welcomed into the lakeFS family, uniting data version control pioneers. The project boasts over 15,500 GitHub stars and is trusted by thousands of users from startups to Fortune 500 companies including Hugging Face, UBS, and others.
DVC also provides a VS Code extension for seamless integration into development workflows, along with comprehensive documentation, courses, and an active community.
Target Audience
Data scientists, ML engineers, AI/ML teams, data engineering teams, and enterprises managing large-scale data infrastructure.
Key Features
- Git-like version control for data and ML models
- VS Code extension for integrated workflows
- Scalable infrastructure for petabyte-scale data (lakeFS)
- Git extension for local data science projects (DVC)
- Free and open source
- Experiment tracking and management
- Support for multimodal object stores and data lakes
- Active community with Discord and GitHub support
Use Cases
- Version controlling ML datasets and models
- Managing data science project workflows
- Enterprise AI data infrastructure management
- Collaborative data science team workflows
- Tracking ML experiments and reproducing results
- Managing large-scale data lakes and object stores
Pros
- Free and open source
- Familiar Git-like workflow reduces learning curve
- Scales from individual projects to enterprise infrastructure
- Strong community support with 15,500+ GitHub stars
- VS Code integration available
- Trusted by major companies like Hugging Face and UBS
Cons
- Two separate products may cause confusion for new users
- Enterprise features require lakeFS which has separate pricing
- Learning curve for teams unfamiliar with Git workflows
Pricing
Free and open source for DVC Git extension. lakeFS Enterprise available with custom pricing for enterprise teams.
