DVC
by Community
🦉 Data Versioning and ML Experiments
OSS
DVC
Added 1 June 2026
Overview
DVC (Data Version Control) is a version control system for machine learning projects that tracks data, models, and experiment metadata alongside code. It integrates with Git to manage large files and pipelines, enabling reproducible ML workflows without storing binaries in repositories.
Best for
Best for
ML teams building reproducible pipelines who need Git-like versioning for data and models
Use cases
- Track dataset versions and model artifacts across experiment iterations
- Reproduce ML pipelines and results from previous runs
- Collaborate on ML projects with versioned data and experiment history
Notes
DVC (Data Version Control) is a version control system for machine learning projects that tracks data, models, and experiment metadata alongside code. It integrates with Git to manage large files and pipelines, enabling reproducible ML workflows without storing binaries in repositories.
15,643 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Track dataset versions and model artifacts across experiment iterations
- Reproduce ML pipelines and results from previous runs
- Collaborate on ML projects with versioned data and experiment history
Pros
- Integrates seamlessly with Git for unified project versioning
- Handles large files and remote storage without bloating repositories
- Tracks full experiment lineage including parameters, metrics, and outputs
Cons
- Requires Python and command-line familiarity for typical workflows
- Learning curve for teams unfamiliar with version control concepts
- Remote storage setup and configuration adds operational overhead
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Integrates seamlessly with Git for unified project versioning
- Handles large files and remote storage without bloating repositories
- Tracks full experiment lineage including parameters, metrics, and outputs
Cons
- Requires Python and command-line familiarity for typical workflows
- Learning curve for teams unfamiliar with version control concepts
- Remote storage setup and configuration adds operational overhead
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