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Provide Self-Serve Access to Infrastructure
On-demand, data scientists can launch Workspaces with the latest NVIDIA GPUs optimized with open source and commercial data science tools, frameworks, and libraries.
Automate time-consuming dev ops chores typically required to launch GPU-accelerated research environments. Attach auto-scaling clusters supporting the most popular distributed compute frameworks - Spark, Ray, and Dask - that dynamically grow and shrink to meet the needs of intensive deep learning and training workloads while saving costs.
Domino and NVIDIA ensure the entire experience is seamless, so data scientists can focus on research while IT teams eliminate infrastructure configuration and debugging tasks.
Orchestrate Workloads Centrally for Improved Productivity
Orchestrate all data science workloads through Domino’s single system of record - across tools, packages, infrastructure, and compute frameworks.
Provide data scientists self-service access to their preferred IDEs, languages, and packages so they can focus on data science innovation.
Reduce IT costs, management, and support burden with tools and NVIDIA infrastructure consolidated and orchestrated in a central location across projects and teams.
Reproduce Work and Compound Knowledge
Track all data science artifacts across teams and disparate tools - including code, package versions, parameters, NVIDIA infrastructure, and more.
Establish full visibility, repeatability, and reproducibility at any time across the end-to-end lifecycle.
Teams using different tools can seamlessly collaborate on a project, with the ability to leverage valuable insights and harvest a flow of collective wisdom.
Streamline Inference & Hosting
Support the end-to-end model lifecycle from ideation to production – explore data, train models, validate, deploy, monitor, and repeat – in a single platform with the latest NVIDIA GPU acceleration capabilities.
After a model or analysis is ready, Domino makes it easy for data scientists to publish it as an API, integrate it in a web app, or deploy it as a scheduled job. Once a model is published Domino can automate the process of data capture needed to monitor drift and ensure ongoing model health.
Professionalize data science through common patterns and practices, with workflows that reduce friction and accelerate lifecycle step and transition, so all people involved in data science can maximize productivity and impact.
Drive Utilization of GPU Resources
Easily provision, share, and manage NVIDIA GPU resources into different hardware tiers supporting a variety of different users and use cases. Set permissions by user groups to ensure that valuable compute resources are efficiently utilized.
With Domino’s support for NVIDIA Multi-Instance GPU (MIG) technology on the NVIDIA A100 Tensor Core GPU, admins can take this even further, allowing up to 56 concurrent notebooks or hosted models, each with an independent GPU instance.
Domino gives IT visibility into GPU hardware utilization. Usage information and tracking can inform a centralized IT team to easily allocate resources and chargebacks, while also measuring the business value that the GPU-enabled model is generating.
Featured Integrations
Domino's close collaboration with NVIDIA means our Enterprise MLOps Platform supports a broad range of NVIDIA Accelerated Computing solutions.
Trusted by Customers Across Industries
Learn how enterprises are building a model-driven competitive advantage with Domino and NVIDIA.
Watch The Video
So many companies tragically waste precious engineering resources trying to build this tooling themselves, and it's a lot harder. Your engineers are going to be creating much more value when focused on problems that are competitively differentiated and unique to your business.
Jim Swanson
CIO, Johnson & Johnson
Fireside Chat: Healthcare
How Johnson & Johnson is embedding data science across their business
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