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DVC (Iterative)

Open SourceFunded

Open source data version control for AI and machine learning workflows

About DVC (Iterative)

DVC (Data Version Control) provides an open source platform designed to manage and version data in AI, machine learning, and data science projects using a Git-like model. It enables teams to apply software engineering best practices to data management, ensuring reproducibility, collaboration, and traceability across complex data workflows. The platform supports individual data scientists with lightweight Git extensions and scales to enterprise needs with robust infrastructure for petabyte-scale data lakes and multimodal object stores.

Targeted primarily at enterprise AI and data engineering teams, DVC facilitates scalable data version control that integrates seamlessly with existing development workflows. Its key value lies in bridging the gap between code and data management, allowing organizations to maintain control over evolving datasets and models while supporting collaboration across distributed teams. The platform's open source nature encourages community contributions and transparency, making it a flexible solution adaptable to diverse AI/ML infrastructure requirements.

Key Capabilities

✓Git-like data version control for AI/ML projects
✓Scalable infrastructure for petabyte-scale data lakes
✓Integration with existing data science workflows
✓Support for multimodal object stores
✓Lightweight Git extension for local workflows

Integrations

GitVS CodelakeFS

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This profile was compiled by CIOPages from public sources with AI assistance, and may be incomplete or out of date. It is informational only and not an endorsement. Represent this vendor? or .

Quick Facts

dvc.org

CategoryAI & ML Platforms

SubcategoryML Platforms & MLOps

PricingSubscription

DeploymentOpen Source

Target SizeEnterprise

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