Critical Success Factors for Data Science Teamwork
Read cases in which collaboration and data science teamwork isn’t just optional but necessary, and how to do it right.
Data science teamwork
that scales
Quickly reuse, build and share projects -
track experiments, visualize pipelines, store and version
anything.
Automatic workflows. Integrated Git, DVC & MLflow. Open core.
Deploy your first project in 3 minutes. Get started now
Create winning models together
Tackle hard challenges with confidence by connecting with collaborators
Integrated Stack
Build your career
on the latest stack
- Automate mundane tasks on top of a best in class stack DVC-Git-MLFlow
- Track and push data without devops
- Integrate with the tools you love or start with instant infra
- Start quick with a familiar interface and project templates
- Store large data sets and code in the same place
Automated Workflows
Understand every change,
recreate every
result
- Keep everything in sync from pipelines to experiments
- Visualize complex projects and demonstrate progress
- Diff and instantly see all the changes that matters
- Iterate faster with true reuse
- Communicate results crisp and clear
Hands on Community
Discover the ML community
you always needed
- Connect with a community focused on real world DS
- Perfect your model then contribute back to build something bigger
- Find reference work you can trust
- Carry and showcase your work through your career
- Get recognition and grow as an expert
How it works?
1Sign Up
Start by forking or pushing an existing project, or start a new one from a template
2Consolidate
Models, data, code, experiments & more are automatically combined into a single interface
3Collaborate
Add collaborators or contribute back to open source data science projects
Supports tools & frameworks you already use
DAGsHub uses open source protocols, so it's fully portable and extensible
Or any other tool you like...
Who's it for?
DAGsHub is empowering open source in the data science world
1
You're collaborating with a large or growing team of data scientists
2
You want to work with the best of breed open source tools
3
You need automation for data versioning or rapid iteration
4
You're working with large or changing data for your project
5
Git is your go to tool for versioning
6
You’re a contributor and believe in the power of teams
