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