Amazon Web Services (AWS) has offered its general cloud compute and storage capabilities to a range of verticals for a while now. But at this year’s re:Invent conference, things took an interesting turn as it began introducing custom built solutions tailored to meet the needs of specific industries.
Chief among its new offerings are its IoT FleetWise and IoT TwinMaker products, though it also took aim at the financial services segment through partnerships with Goldman Sachs and Nasdaq. Moor Insights and Strategy senior analyst Will Townsend told Fierce AWS’s initial services strategy targeting the automotive and industrial segments is “solid.”
“From my perspective, industrial automation is likely the biggest addressable market followed by transportation and logistics, so they did their homework,” he said.
AWS GM for Industrial IoT and Edge Mike MacKenzie told Fierce TwinMaker’s launch is reflective of a pivot the company made this year to home in on industrial IoT as a “major focus”. The decision, he said, was a no brainer.
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MacKenzie explained the industrial sector was one of the first to look at consumer IoT products and imagine ways in which these could be applied in their own environments. For instance, how a Roomba might be repurposed to carry parts across a factory floor. An ecosystem of sensors and other products quickly sprang up and as more added cloud connectivity capabilities, companies began looking for ways to make their IoT devices more useful, he said. AWS was a natural partner for their IoT ambitions given it was already delivering IT services to many companies in the segment.
Both MacKenzie and AWS VP of Engineering Bill Vass said machine learning (ML) is at the heart of what Amazon is trying to do in the space. The former noted that ML can help companies with hundreds of locations compare performance across sites to preemptively diagnose potential equipment issues and spotlight operational inefficiencies. Vass added that ML also makes it easier for companies to sift through the massive amounts of data coming in from their sensors and, coupled with edge compute capabilities, can allow them to react faster than ever before.
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Machine learning implemented at the edge can also eliminate the need for companies to invest in sophisticated sensors by turning cameras into “very advanced IoT devices,” Vass said. Now, a $20 camera can be used to measure vibrations, detect smoke, flag changes in gauge measurements and more. When deployed as part of AWS’ new digital twin service, these same cameras can provide a live video feed of the environment when problems arise, he noted.
MacKenzie said this technology can clearly be applied in other areas, for instance to help create smart spaces and buildings in a post-Covid world. With ML, cameras can be used to ensure social distancing in common areas and automatically move meetings to larger rooms if the space becomes crowded.
He added the next natural step for AWS beyond this could be a foray into smart cities or smart monitoring for power and utility companies with extensive networks. But the possibilities are endless, he said.
“I think we just keep building technology and tools that people can use to solve any use case,” MacKenzie concluded.