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    Domino, in partnership with NVIDIA®, supports open, collaborative, reproducible model development, training, and management free of DevOps constraints - powered by efficient, end-to-end compute. Democratize GPU access by enabling data science teams with powerful NVIDIA AI solutions - on premises, in the cloud, or in the modern hybrid cloud.

    Provide Self-Serve Access to Infrastructure

    Launch on-demand workspaces with the latest NVIDIA GPUs, optimized with open source and commercial data science tools, frameworks, and libraries - free of dev ops.

    Attach auto-scaling clusters that dynamically grow and shrink - using popular compute frameworks like Spark, Ray, and Dask - to meet the needs of intensive deep learning and training workloads.

    Data scientists can focus on research while IT teams eliminate infrastructure configuration and debugging tasks. 


    Orchestrate Workloads Centrally for Improved Productivity

    Domino acts as a 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, train, validate, deploy, monitor, and repeat – in a single platform - with the latest NVIDIA GPU acceleration capabilities.

    Domino makes it easy for data scientists to publish models - as an API, integrated in a web app, or deployed as a scheduled job - while monitoring drift and ongoing health.

    Professionalize data science through common patterns and practices with workflows that reduce friction, so all teams involved in data science can maximize productivity and impact. 


    NVIDIA Page Streamline

    Drive Utilization of GPU Resources

    Easily provision, share, and manage NVIDIA GPU resources. Set permissions by user groups and use case to ensure 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 allow 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 enables IT to easily allocate resources and chargebacks while also measuring ROI.



    Trusted by Customers Across Industries

    Learn how enterprises are building a model-driven competitive advantage with Domino and NVIDIA

    Analytics Center of Excellence in Insurance

    Weaving fact-based decision-making into the fabric of their organization.
    Watch panel
    Scaling for Innovation Across the Healthcare Value Chain

    How to deploy data science at scale - from organizational strategy to infrastructure and tooling.
    Watch panel
    Lockheed Martin
    Anomaly Detection in Aerospace & Manufacturing

    Applying leading-edge data science to push the bounds of rocket science.
    Watch webinar
    Natural Language Processing in Insurance

    Model-driven policy approvals 800x faster than traditional approaches.
    Watch webinar
    Johnson & Johnson / Janssen
    Image Classification in Pharma & Biotech

    10x faster development of deep learning models to deliver precision medicine.
    Read case study
    Predictive Maintenance in Energy & Utilities

    Manage power generation equipment, optimize liquid gas shipping, make hydrology predictions, and more.
    Watch webinar

    Domino & NVIDIA Perspectives | Scaling MLOps in the Enterprise

    On-Demand NVIDIA GTC Panel
    A Vision for Kubernetes as the Foundation for Enterprise MLOps

    Learn from innovators in Kubernetes and GPU-accelerated data science.

    On-demand NVIDIA GTC Panel
    How Johnson & Johnson is Embedding Data Science Across its Business

    Discussion featuring Johnson & Johnson CIO, Jim Swanson

    Featured Integrations

    Domino's close collaboration with NVIDIA means our Enterprise MLOps Platform supports a broad range of NVIDIA Accelerated Computing solutions.

    Try Domino on NVIDIA LaunchPad for free!

    Get immediate, short-term access to a curated lab with Domino on NVIDIA AI Enterprise.

    Technical Resources

    Technical Webinars

    Virtualize GPU-accelerated Data Science and AI Workflows in Your Data Center with Enterprise MLOps

    March 2022, NVIDIA GTC Session


    Breaking Down Silos Across Simulation, Analytics, and AI to Scale Innovation

    November 2021, NVIDIA GTC Session

    TCS HPC A3 Solution

    Beyond Spark: Dask and Ray as Multi-node Accelerated Compute Frameworks

    November 2021, NVIDIA GTC Session


    Visual Target Recognition from Raw Data to NVIDIA Jetson with MATLAB and Domino

    April 2021, NVIDIA GTC Session


    Slash time spent on model training and tuning. Unleash multi-node GPU acceleration using Ray and PyTorch!

    April 2021, NVIDIA GTC Session


    Running complex workloads using on-demand GPU-accelerated Spark/RAPIDS clusters 

    April 2021, NVIDIA GTC Session



    Data Science Blogs

    Feature extraction and image classification using Deep Neural Networks and OpenCV

    Dr. Behzad Javaheri, March 24, 2022


    Speeding up Machine Learning with parallel C/C++ code execution via Spark

    Nikolay Manchev, February 16, 2022


    Powering Up Machine Learning with GPUs

    Dr. J Rogel-Salazar, December 3, 2021


    Introduction to Deep Learning and Neural Networks

    David Weedwark, November 18, 2021


    Spark, Dask, and Ray: Choosing the Right Framework

    Nikolay Manchev, September 7, 2021