Cisco - Kaustabh Das, vice president of Product Management, Data Center Group
“Cisco and NVIDIA are collaborating on AI/ML software stacks on NVIDIA GPU-optimized Cisco UCS platforms to simplify and accelerate AI/ML workload deployment. We are excited to learn that, with RAPIDS, NVIDIA is expanding their GPU applicability with accelerated software stacks to address traditional machine learning and big data analytics. We look forward to the possibilities for our GPU-accelerated server portfolio, including the recently launched Cisco UCS C480 ML M5 Rack Server, a best in class, purpose-built server with eight NVIDIA V100 GPUs and NVIDIA NVLink interconnect.”
Dell EMC - Ravi Pendekanti, senior vice president of Product Management and Marketing, Servers & Infrastructure Systems
“Dell EMC is committed to providing our customers with world-class IT infrastructures that enable them to gain real, competitive business advantage. We work with our ecosystem partners to ensure our customers have the latest data science tools available to help them transform data insights into business outcomes. Our goal is to combine the new GPU-accelerated open-source data science software from NVIDIA with our portfolio of NVLink-enabled Dell EMC PowerEdge servers to significantly accelerate the fields of machine learning and big data analytics.”
FASTDATA.io - Alen Capalik, founder and CEO
"The RAPIDS open-source project launched by NVIDIA is going to revolutionize the data science pipeline. At FASTDATA.io, we're excited that our Plasma Engine — the first software to fully leverage NVIDIA GPUs for real-time processing of infinite data in motion — will play a part in that revolution."
Georgia Tech - David Bader, professor
“Georgia Tech is excited to contribute to RAPIDS, an open-source playground for NVIDIA GPU-accelerated analytics. In this age of massive data, our contribution to the RAPIDS graph libraries will help data scientists gain meaningful knowledge from ever-growing datasets.”
Graphistry - Leo Meyerovich, co-founder and CEO
“Graphistry, one of the first GPU cloud startups, has been quietly bringing new levels of visibility to sensitive F500 and federal teams that must comb through records in finance, cybersecurity, operations, and sales. As an early contributor to RAPIDS and a force behind Apache Arrow, Graphistry has taken a big bet on RAPIDS. The firm is already known for having redefined the visual compute fabric to be a real-time blending of browser and cloud GPUs, and is working with the RAPIDS team to add next-level tabular analytics to its existing graph GPU visual analytics core."
H2O.ai - Sri Ambati, founder and CEO
“Machine learning is transforming businesses and NVIDIA GPUs are speeding them up. With the support of the open source communities and customers, H2O.ai made machine learning on GPUs mainstream and won recognition as a leader in data science and machine learning platforms by Gartner. NVIDIA's support of the GPU machine learning community with RAPIDS, its open-source data science libraries, is a timely effort to grow the GPU data science ecosystem and an endorsement of our common mission to bring AI to the data center. Thanks to our partnership, H2O Driverless AI powered by NVIDIA GPUs has been on an exponential adoption curve — making AI faster, cheaper and easier."
INRIA (scikit-learn) - Gael Varoquaux, director of Scikit-Learn Operations
"NVIDIA is demonstrating real progress in accelerating data science with new productivity tools such as RAPIDS. Combining very fast computation in a high-language is a game changer for data-analytics teams. We are excited that NVIDIA has chosen to make RAPIDS compatible with scikit-learn. We believe that it can benefit our community and look forward to collaborating with NVIDIA."
Kinetica - Nima Negahban, co-founder and CTO
“The RAPIDS suite of open-source libraries is a significant improvement in enabling data scientists to leverage the power of the GPU across their model development toolchain. RAPIDS can dramatically simplify and optimize training and improve model accuracy, without any significant logical redesign effort on the part of the data scientist. We’re excited to partner with NVIDIA in this journey to democratize AI — with NVIDIA driving model development and training and Kinetica driving operationalization and deployment of those models, enabling enterprises to gain maximum insight from their data.”
Lenovo - Kirk Skaugen, president of Data Center Group
“Enterprise customers and academia continue to be challenged in working with and analyzing massive amounts of data as they develop and test new strategies. The new RAPIDS open-source software promises to accelerate workflows by running them end-to-end on NVIDIA GPUs. We believe this innovation and collaboration will make a significant impact for customers.”
MapR - John Schroeder, CEO
“RAPIDS is a breakthrough announcement for data science and, more importantly, the ability to directly impact an organization with data science. MapR is supporting this effort by focusing on complementary data management and deployment activities to accompany the end-to-end RAPIDS data science training and model workflow.”
NERSC - Rollin Thomas, Python data analytics lead
"NERSC supports more than 7,000 researchers at universities, national labs and in industry. They increasingly want productive, high-performance ways of interacting with their data from complex science simulations or experimental and observational facilities like particle accelerators and telescopes. We look forward to working with NVIDIA to put new high-performance Python data analytics tools like RAPIDS in the hands of our users to accelerate their pace of discovery across many scientific disciplines."
NetApp - Octavian Tanase, senior vice president of ONTAP
“Organizations must take advantage of new artificial intelligence capabilities to drive competitive advantage and accelerate digital transformation. The combination of RAPIDS powered by NVIDIA GPUs and NetApp’s AFF A800 cloud-connected all-flash storage will help customers confidently tap into growing data resources with virtually unlimited scalability and performance needed to feed, train and operate data-hungry AI applications.”
NumFOCUS - Andy Terrel, president of the board of directors
"NVIDIA’s support of NumFOCUS represents an investment to the community. As two leaders in data science, we feel our work together will bring better tools to science and business alike."
OmniSci - Todd Mostak, CEO and co-founder
“Data scientists use OmniSci on NVIDIA GPUs to accelerate data exploration and feature engineering when creating machine learning models. Now our users can interactively query and visualize data at scale in OmniSci, and then pipe the results into RAPIDS’ open-source libraries, enabling powerful end-to-end data science workflows. Together, NVIDIA and OmniSci make it much faster to build and iterate on models, resulting in increased accuracy and quicker time to deployment.”
Pure Storage - Matt Burr, general manager of FlashBlade
"Our customers look to data for insights that separate them from the competition and deliver ever-increasing value for their end users. RAPIDS amplifies the impact of NVIDIA GPU acceleration and Pure Storage FlashBlade for data science and machine learning workflows to help more data scientists speed their training pipelines while maintaining optimal low-latency performance for faster time to results.”
Quansight - Travis Oliphant, NumPy and SciPy creator, co-founder and director of Anaconda, founder and CEO of Quansight
"NVIDIA has long been a leader in accelerated tools for advanced analytics and has consistently offered freely available high-speed libraries for use by developers in the data-science community. I am thrilled to see their expanded open-source framework for data-science and their commitment to an end-to-end software and hardware solution. These innovations will enable a dramatic speed-up of the entire data-science workflow and unleash innovation across the broader open-source ecosystem."
SAP - Juergen Mueller, chief innovation officer
“SAP has worked with NVIDIA closely over the past several years to take advantage of GPU acceleration for many SAP Leonardo Machine Learning-enabled solutions. We are furthering that collaboration now to explore the possibilities offered by RAPIDS, which promises to hypercharge data science pipelines on GPUs. This is an important step to accelerate data science and machine learning for data scientists as we bring intelligence to enterprises with SAP Leonardo and SAP HANA.”
