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Solutions for Every Stage of Your AI Journey

New or Getting Started in AI?

Machine Learning, Deep Learning and Artificial Intelligence are transforming industries and delivering benefits across a wide range of applications – from customer service to health care, from maintenance to manufacturing.

Artificial Intelligence applications are not replacing humans. Rather AI is providing faster ways to analyse data and support better faster decisions.

If you’re new to AI or just getting started, XENON can help with:

  • Consulting services to choose the right approach and ensure you get results from your first AI projects.
  • Advice on AI models, data frameworks and processing options (cloud to workstations to data centres).
  • Advice on how to grow your AI practice and plan for next steps.

There are a range of free resources on the XENON website to help you get started:

Contact Us today for a free consultation to get started in AI.

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Data Scientist Working on AI?

You’ve taken the first step, done a POC or initial AI projects. Now you’re looking to build on what you’ve done and apply machine learning or deep learning models in the wild.

At the point you need to contain cloud costs, develop larger data sets or go faster – you want to look at dedicated GPU accelerated hardware.

Deep Learning and Machine Learning models are compute intensive, especially when model training. XENON delivers a range of solutions for individual Data Scientists and teams working on AI projects. These solutions include:

These solutions are suitable for individuals, or small teams of data scientists. The Multi-instance GPU capabilities of the NVIDIA A100 makes these platforms agile and highly capable for data analytics, training and inference work. Graphcore excels at a range of AI processing tasks.

XENON provides un-biased advice on the best hardware and software solution most suited to your AI applications, so you can take the next step in growing your AI practice.

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Growing AI Practices = Growing IT Requirements

As individuals and small teams scale up and initial projects deliver positive impacts from AI implementations, a few things happen:

  • AI proliferates through an organisation – this increases the number of AI models being developed and deployed.
  • More models require more data for analysis and training. All the data needs to be easily and quickly accessible so there are no delays in AI training.
  • The are increasing demands on the AI compute infrastructure as more projects compete for limited processing cycles.

These growing pains can often result in an exponential growth in demands on IT resources – data storage, network traffic and compute/GPU/processing cycles.

XENON has experience taking organisations on the journey from cloud POC, to workstations and small teams, to full clustered architectures with DGX systems, NVIDIA PODs and SuperPODs (Clusters of 5+ DGX servers) and Bow Pods from Graphcore.

XENON assists companies to make these transitions and step jumps in capabilities with planned growth in resources, no fork-lift upgrades, and a deep understanding of the whole stack so that data storage, networking and compute resources are optimised.

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