TECHNOLOGY
“ Luckily , running AI workloads creates telemetry data that could be used to automate decision making and boost efficiency ”
CHARLIE BOYLE VP , GM DGX SYSTEMS ,
NVIDIA companies adopting AI tend to take a mixed approach ,” says Charlie Boyle , VP and GM of DGX Systems at NVIDIA , adding that the growth of IoT is driving adoption of this mixed approach , in which decision-makers use the “ public cloud , like AWS , Azure and Google Cloud , and private clouds in on-premises servers to deliver applications with lower latency , in industry parlance , to customers and partners while maintaining security by limiting the amount of sensitive data shared across networks .”
Boyle explains that , as customers increasingly adopt AI , they ’ re beginning to look at not just how to run their workloads , but also how to optimise and automate them . Finding the right place in which to run an AI workload - whether it ’ s deep learning model-training , or inference , which applies the trained model to an application - is a key problem to solve in order to increase efficiency . “ Luckily ,” says Boyle , “ running AI workloads creates telemetry data that could be used to automate decision making and boost efficiency .”
This telemetry data can then be used to monitor efficiency , data location and system availability . Combining it with
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