- Move compute tasks off desktops onto powerful, centralized hardware — in the cloud or on premise
- Access GPU hardware for deep learning
- Track and run multiple experiments in parallel to speed up projects.
- Domino runs your code in Docker containers you can configure to create shared, reusable, revisioned Compute Environments
- Spin up interactive workspaces with one click — using the tools you already know and love, e.g., Jupyter, RStudio, Zeppelin.
- Run and track batch experiments in any language you want, even commercial languages like SAS or Matlab if you have licenses.
- Run multiple experiments in parallel, then track, filter, and compare them.
- Ships with Domino Analytics Distribution (includes database drivers, Anaconda Python, popular deep learning packages, visualization packages, etc.) Or customize your own environment.
- Find, share, and reproduce past results.
- Discuss work with collaborators and stakeholders in one place.
- Search and discover past work before starting new projects
- Control access with granular permissions and LDAP integration
doneRollback to old versions of models
doneSplit traffic across versions to do
doneManage security for consuming and modifying deployed models
Share Reports and Dashboards
Expose results to stakeholders when BI tools don’t cut it
- Publish visualizations using open source data science tools, including knitr, htmlwidgets, plotly, D3, etc.
- Publish interactive dashboards and web apps using Shiny and other tools
- Schedule recurring tasks to update reports — serve results through the web or send to stakeholders via email
- Control access to dashboards and reports behind a central security layer