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DVC (Data Version Control) is an open-source tool designed to bring software engineering best practices to data science and machine learning projects. Built by the team at lakeFS, DVC solves the problem of managing large datasets, models, and experiments alongside code. It uses a Git-like model to version control data, enabling reproducibility, collaboration, and tracking of data pipelines. Core capabilities include data versioning, pipeline management, experiment tracking, and integration with cloud storage. DVC is ideal for individual data scientists and small teams working on data science projects, as it applies data version control with minimal overhead. It also offers a VS Code extension for seamless integration into development workflows. For enterprise needs, lakeFS provides a highly scalable data version control infrastructure for complex AI operations and big data environments.
4.5/5
High, consistent, and on-brand
Clean and minimal
Data version control for AI/ML and data science projects
Integrates with Git, cloud storage, and VS Code
Free and open source
Features with the highest value for users are highlighted here.
Git-like data versioning
Experiment tracking and management
Pipeline management with dependency tracking
VS Code extension integration
Open source and free to use
Seamless Git integration for large files
Model and dataset version control
Reproducible ML workflows
Data Scientists
Machine Learning Engineers
Data Engineers
Research Teams
Startups
Enterprise AI Teams
Open Source Contributors
Academics
Freelance Data Consultants
DevOps Engineers
DVC (Open Source)
lakeFS Enterprise
Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
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