Not having to move around large amounts of data, explains Rivera, results in enhanced security, lower latency, and increased reliability. Although larger AI and machine learning models will still require the compute power of the cloud or a data center, smaller models can be trained and deployed at the edge. The benefits of implementing an edge-computing architecture are operationally significant. “The type of processing that needs to happen in near real time is not something that can be hauled all the way back to the cloud in order to make a decision,” says Sandra Rivera, executive vice president and general manager of the Datacenter and AI Group at Intel. And this is where edge computing comes in, because as companies pursue faster, more efficient decision-making, all of that data needs to be processed locally, in real time-on device at the edge. The internet of things and smart devices are everywhere, which means computing needs to be everywhere, too.
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