AI/ML at the Edge: 4 things CIOs should know

And latency almost always matters when it comes to running artificial intelligence/machine learning (AI/ML) workloads. Great AI requires a lot of data, and it demands it immediately." That's both the blessing and the curse in any sector – industrial and manufacturing are prominent examples, but the principle applies widely across businesses – that generates tons of machine data outside of their centralized clouds or data centers and wants to feed it to an ML model or other form of automation for any number of purposes. Whether you're working with IoT data on a factory floor, or medical diagnostic data in a healthcare facility – or one of many other scenarios where AI/ML use cases are rolling out – you probably can't do so optimally if you're trying to send everything (or close to it) on a round-trip from the edge to the cloud and back again. In fact, if you're dealing with huge volumes of data, your trip might never get off the ground. "I've seen situations in manufacturing facilities ...