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.
Software and environment management
The right tools for the job, centrally provisioned
Modern data science teams use dozens of tools and multiple languages every day. Experimenting with new tools is critical, but can be a nightmare to reproduce. This limits innovation and hurts collaboration.
Domino provides one-click access to a wide variety of open-source and commercial tools and languages including Python, R, SAS, and MATLAB. Versions are tracked to avoid conflicts when team members try to reuse work. As technology changes, it's easy to add new emerging tools when needed, future-proofing your data science platform.
Unified data access layer
Easy, governed, and secure access to data
Data is the fuel for all models but data access and preparation is a regular struggle. Not with Domino.
Domino’s secure Data Connectors provide rapid, secure access so you can quickly get to work while also adhering to data access policies. Domino’s Data Access Library unifies access patterns for disparate data types through SQL syntax. Analytic-ready data, along with its associated metadata, is easily saved in a result set for later reuse, saving time and compute costs.
model and app deployment
Easily export production models to infrastructure of choice
Building models is tough enough — deploying them shouldn’t be. Don't waste time translating results into other languages.
With Domino, 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.