Success of AI is in Infrastructure

 In VentureBeat

Artificial intelligence (AI) is bringing many changes to the enterprise, none of which is more vital to its success than infrastructure. Changing the nature of workloads – not just how they are generated and processed but how they apply to the operational goals – requires changes in the way raw data is handled, and this extends right down to the physical layer of the data stack.

As VB pointed out earlier this year, AI is already changing the way infrastructure is being designed all the way out to the edge. On a more fundamental level, basic hardware is becoming optimized to support AI workloads, and not just on the processor level. But it will take a coordinated effort, and no small amount of vision, to configure hardware to handle AI properly – and indeed, there isn’t likely to be one right way of doing it anyway.

How Gap Inc. is leveraging the modern data-stack and building AI to solve age-old customer problems

Foundational change for AI infrastructure

In a recent survey of more than 2,000 business leaders by IDC, one of the lead findings was the growing realization that AI needs to reside on purpose-built infrastructure if it is to bring real value to the business model. In fact, lack of proper infrastructure was cited as one of the primary drivers for failed AI projects, which continues to stymie development in more than two-thirds of organizations. As with most technological initiatives, however, key hurdles to more AI-centric infrastructure include costs, lack of clear strategies and the sheer complexity of legacy data environments and infrastructure.

All hardware is interrelated in the enterprise, whether it sits in the data center, the cloud or the edge, and this makes it difficult to simply deploy new platforms and put them to work. But as tech author Tirthajyoti Sarkar points out, there are plenty of ways to gain real value from AI without the latest generation of optimized chip-level solutions entering the channel.

Cutting-edge GPUs, for example, may be the solution-of-choice for advanced deep learning and natural language processing models, but a number of AI applications – some of them quite advanced, such as game theoretics and large-scale reinforcement learning – are better-suited to the CPU. And since much of the heavy-lifting in AI development and utilization is typically performed by front-end data conditioning tools, choices over cores, acceleration technologies and cache may prove more consequential than the type of processor. ... '