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DVC

Tool Details

Features
Dataset versioning, experiment tracking
Best For / Use Case
Developers
Integrations
Git, AWS
Platform Compatibility
Cloud, Local
Pricing Model
Free

Description

DVC (Data Version Control) is an open-source data and model versioning system bringing Git-like version control to machine learning projects.

It addresses a key MLOps challenge: managing large datasets and complex models alongside source code to ensure reproducibility.

DVC stores pointers in Git while actual data resides externally (cloud or local storage).

Key features include robust dataset versioning, experiment tracking for managing model runs, and ensuring full reproducibility of ML experiments.

By integrating with Git, DVC enables easy switching between data/model versions, enhanced collaboration, and transparent, auditable pipelines, essential for reliable, scalable AI systems.

Last Updated: August 1, 2025
Disclaimer: Tools evolve faster than you can say “AI.” While this info was accurate when posted, features and pricing may change. Visit the official site for the freshest details.

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