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NVIDIA Data Science Workstations from Scan Computers

Tags: NVIDIA (NASDAQ:NVDA), SCAN, Intel (NASDAQ:INTC)

Quick Link: HEXUS.net/qaeej5

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PRESS RELEASE

Following the announcement at NVIDIA’s GTC in San Jose earlier in the year, Scan Computers were named as an exclusive system builder for the Data Science Workstation (DSW). 

Organisations are investing in Data Science for deep learning and AI to improve their time to market, increase efficiency in their organisation and to unlock the value from their combined data sets.  Powered by the latest NVIDIA TuringTM GPU Architecture and enterprise-grade NVIDIA Quadro GPUs, Scans Data Science Workstations are purpose built for data analytics, machine learning (ML) and deep learning (DL).

These systems are tested and optimised with data science GPU-accelerated software such as RAPIDS, tensorflow, PyTorch and Caffe.  They allow companies to create Minimum Viable Products (MVP) with their data sets faster, producing effective model training and demonstrate data visualisation and are the perfect stepping stone before customers invest in a DGX system. 

Scan Data Science Workstations can be evaluated online via a Proof of Concept.

Visit https://www.scan.co.uk/buying-guides/deep-learning/data-science-workstations to find our more.

About Scan:

At Scan we are all about three things – technology, people, and how we bring them together and for over 30 years we’ve been driven by these three passions.  We work with customers and partners to tackle the latest cutting edge areas in IT and bridge the gap these challenges present.

Building on our reputation within the computing industry, we have used this knowledge and experience to provide specialist IT solutions for business verticals including healthcare, financial services, retail, education, AEC and manufacturing.

Scan are also a leader in the world of AI & Deep learning solutions and work closely with key partners in the industry including NVIDIA.  As an Elite Partner, a DLI provider, Scan have also been awarded with Innovator in the Year Award 2018.