Staging environments are as simple as git branches.

Develop your machine learning models and deploy to staging environments from your personal git branch. When you're ready for production, just send a merge request.

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Deploy your model with mb.deploy() or deploy it with git push.

Notebook users love to deploy with one line of Python directly from the notebook. IDE users love to deploy with git push on the command line. Modelbit makes both workflows work seamlessly.

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GitHub and Gitlab sync built in.

Your model code and data are yours. Keep them side-by-side with all your other code in git. All your models and deployments in Modelbit are automatically stored in GitHub, Gitlab, or your own git repository.

Sync GitHub Sync Gitlab

CI/CD and Code Review with your favorite git-based tools

Code review in GitHub Pull Requests or Gitlab Merge Requests. CI/CD with GitHub Actions or Gitlab CI. When your models and deployments are just code in your git repo, all your git-based tools work out of the box.

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