Dagshub : Version control and collaboration for AI experiments
Dagshub: in summary
DagsHub is a platform built for data versioning, experiment tracking, and collaboration in machine learning projects. Designed on top of open standards such as Git, DVC (Data Version Control), and MLflow, it provides a GitHub-like interface tailored to data science and ML workflows, helping teams track data, models, and experiments in a unified and reproducible way.
It is used by researchers, ML engineers, and data teams who need better coordination, transparency, and version control across their projects. DagsHub is particularly suitable for open science, reproducible AI research, and multi-user collaboration.
Key benefits:
Combines code, data, models, and experiments in one versioned repository
Supports collaborative ML workflows with detailed tracking
Built on open tools, making it easy to integrate and adopt
What are the main features of DagsHub?
Data and model versioning with DVC
Integrates Data Version Control (DVC) to track datasets and model files
Manages large files efficiently through remote storage backends
Enables differencing and rollback of data and model versions
All changes to data are tracked and auditable, just like code
Experiment tracking and comparison
Supports MLflow integration to log hyperparameters, metrics, and artifacts
Displays experiment results in a clear, interactive table view
Enables run-to-run comparison of performance and configurations
Keeps experiments linked to data and code versions for full reproducibility
Collaborative interface with Git-style workflows
Built on top of Git repositories, familiar to developers
Includes pull requests, issues, diffs, and discussions for team collaboration
View data, metrics, and experiment outputs directly in the web interface
Enables transparent review of changes to code and datasets
Visualization of data pipelines and file structure
Shows data lineage and pipeline flow for DVC-tracked projects
Helps users understand how datasets and models evolve
Interactive file tree and diffs for both code and data changes
Makes reproducibility and debugging easier in complex workflows
Public and private project support
Suitable for both open science and private enterprise projects
Allows teams to control access, share reproducible projects, and publish results
Simplifies collaboration between researchers, contributors, and reviewers
Why choose DagsHub?
Combines version control, data management, and experiment tracking
Encourages reproducible and transparent AI research
Uses familiar open-source tools like Git, DVC, and MLflow
Ideal for team-based workflows and long-term project tracking
Supports both academic and commercial machine learning projects
Dagshub: its rates
Standard
Rate
On demand
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