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VMware: AI ML is an Emerged Technology - The Importance of Timing

Guest blog by Matt Steiner, NEMEA Business Solutions Strategist, VMware as part of the Digital Transformation In the Public Sector week. #techUKDigitalPS


Building new solutions with emerging technology is exciting.  Emerging technology can often be an underpinning enabler for a particular Digital Transformation project.  Take a step back, however, and think about whether it was the Emerging Technology that underpinned a project’s success, or whether the Timing of that technology was the key to success, and you’ll see that it is almost always Timing.

One area where this is most apparent is in the mainstream adoption of AI/ML technologies.  AI and ML have been around for a long time, so why hasn’t every organisation been able to harness the power of AI/ML solutions to deliver transformational projects?  Why is it that it is only recently, that we have started to see an acceleration of AI/ML adoption?  The answer is Timing.

With AI/ML, there are three aspects that have come together at the same time – the AI/ML planets have aligned!   They are Commoditisation, Economics and Partnerships.


Historically, the creation of AI/ML models has been an extremely specialised task, involving data scientists building everything from scratch.  Recently, however, many pre-defined models for particular use cases have been made available, almost ‘out of the box'.  They have become, to an extent, commoditised.  This makes it easier for many organisations to start on their AI/ML journey – the barriers to entry are considerably lowered.


The main limiting factor to how much AI/ML you can undertake is how much computing capacity you have, and that is usually limited by how much you can, or are prepared to spend, on computing capacity for a given use case. 

In the past, AI/ML was limited to those with access to huge supercomputers.   As costs came down, it started to become viable for Cloud providers to offer AI/ML services.  For many use cases, however, the costs could be eye-wateringly high, particularly if you were taking many iterations to build a working solution.   Latterly, however, the economics of the GPU engines that underpin many AI/ML platforms have had a step change in cost per unit of compute.

The economics have shifted to the mainstream.


So, the entry point to AI/ML is much more accessible and the Economics now work for many more use cases, what else is needed?

The partnership between NVIDIA and VMware is the final piece of the jigsaw puzzle for many organisations.   Most organisations have built infrastructure with VMware technology at the core, and NVIDIA have been a leader in providing the latest GPUs to support new AI/ML workloads.VMware have partnered with NVIDIA to ensure their GPU technology works seamlessly in their platform, allowing organisations to take advantage of the leading technologies from both companies.

This means AI/ML workloads no longer need to be seen as ‘specialised’, needing separate silos of hardware, they just become another ‘Thing’ that can be run on GPU enabled infrastructure.   (To learn more about ‘Things’, this blog article is an excellent primer)


In summary, when it comes to adopting technologies such as AI/ML, the importance of timing cannot be underestimated.  That AI/ML is still considered ‘Emerging’, despite the core technologies being around for decades, is testament to this.

The key to success has been for organisations to understand WHEN the time is right to embark on AI/ML projects or journeys.  For many organisations, the technology planets have aligned, and the time is now!

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matthew steiner, vmware.jpeg

This blog was written by Matthew Steiner - NEMEA Business Solutions Strategist at VMware, and Robbie Jerrom - Principal Architect Apps & ML platforms - Office of the CTO. 

To learn more about Matthew Steiner, please visit his LinkedIn page to get connected.

To learn more about VMware, please visit their LinkedIn and Twitter.

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