Provision scalable compute resources with one-click, in the cloud or on premise. Eliminate DevOps tasks with access individual or shared environments that include any language, package, or tool your team needs: R, Python, SAS, H2O, Tensorflow, Jupyter, RStudio, DataRobot, and more. Scale projects across domains with connectors to all popular data sources, including Spark, Hadoop, data warehouses, and more.
Remove the “last mile” problem in model delivery with a multi-modal approach that ensures models fit into business use cases, whether as a real-time API, a visualization, or a self-service web app.
Enable teams to deploy and iterate models with streamlined model deployment, monitoring, and production versioning.
Domino captures metrics and engagement to help you measure their impact and drive iterations.
Remove the opaqueness of model development on laptops with a single holistic view of your team’s data science projects. Domino provides a central interface where you can check progress on work, understand costs associated each project, leave comments, and see the latest results, usage and performance—without interrupting your team or transacting over email.
Eliminate your team’s wasted time trying to reproduce lost work with Domino’s Reproducibility Engine that automatically tracks all data science experiments—code, data, environment details, parameters, discussions and results.
Domino provides a Knowledge Center that creates a virtuous cycle of feedback and collaboration across your team and both IT and business stakeholders. Anyone can share insights and find artifacts from all projects, so future work can continue from past work, and learnings are set free from individual laptops.
Get new hires productive on day one with pre-built compute and tool environments, and with tutorials based on past projects and organizational learnings. Domino allows any team member to quickly pick up where others left off, with each project's artifacts and history.
Domino is your team’s single collaborative platform for data science work, archived and tracked for discovery.