Self-serve elastic compute
One-click access to scalable compute
Say goodbye to DevOps learning curves and wait times. Stop trying to guess compute needs in advance. With Domino, you can self-serve dynamically adjusting Kubernetes-based compute clusters with just a few clicks. You can easily access distributed frameworks such as Spark, Ray, and Dask, as well as NVIDIA GPUs, to power the most computationally hungry algorithms.
IT immediately benefits from centralized infrastructure management that optimizes resource use and facilitates chargeback to business units based on usage.
Accelerate Research with Elastic Compute
Unlock innovation by experimenting 10x faster.
Getting data scientists quickly to work on solving business problems is key for scaling data science and driving business impact.
Domino’s data science workbench provides the flexibility and power that data scientists need to accelerate research. They are free to use the tools they want, on hardware optimized for the task at hand, in a governed and scalable environment that fosters reproducibility, reusability, and collaboration. With elastically scaled compute at their fingertips, they can run hundreds of machine learning experiments in parallel and easily compare the results, maximizing their productivity.
Model and App Deployment
Deploy to Production at Scale
With Domino, you can accelerate processes to create, manage, scale, and secure production models. Models are easily deployed through APIs, Apps, and Launchers, or exported as Docker images to CI/CD pipelines, AWS Sagemaker, or other infrastructure. Interactive, scalable Apps created with Shiny, Dash, and Flask make it easy for non-technical users to interact with models.
Domino handles horizontal scalability, high availability, security, and everything else you shouldn't need to worry about.
Resource Management & Cost Control
Domino automatically spins down machines when analyses finish, so you avoid runaway costs when users forget to stop them. Configure limits on different types of hardware to avoid unexpected costs. View utilization at a point in time or historically by user and project and track compute costs across different projects and users for internal chargeback.