The edge just may be where the action is. And the edge is double-edged: It could be a distributed corporate network, or it could be an application running within a small device.
At least 44% of organizations are investing in edge IT to create new customer experiences and improve engagement, according to IDC. Two-thirds (66%) indicated that they were planning to run artificial intelligence (AI) and machine learning applications at the edge at the time of the survey. Overall, investment in edge infrastructure will grow at a compound annual growth rate of almost 23%, IDC adds.
Also: Ready to upskill? Look to the edge (where it’s not all about AI)
This means opportunities at many points across the more decentralized end of the information technology spectrum, which is emerging as the go-to zone for applications.
“Think of edge as a cloud layer for a specific location that matters for a growing set of use cases that are time-sensitive and data-intensive,” Mike Zirkle, VP of 5G commercialization and ecosystem for Verizon Business, told ZDNET. “We see it work well with autonomous mobile robots, autonomous guided vehicles, and shop-floor automation, which are on-prem environments.”
Zinkle also pointed to emerging “near-edge” and “far-edge” environments. Near-edge examples include “transportation, virtual roadside units, transactions for tolling, congestion management, and mobility use cases.”
For far edge, he added, “there are things like broadcasting modernization for closed captioning, which has to do with improving the delay on closed captions during live events, so the captions better match the pace of speech.”
At the corporate edge, there has been a movement of AI and other intelligent processing workloads away from centralized systems. “Companies have started leveraging advances in networking, algorithms, and edge computing to run artificial-intelligence workloads outside of data centers and closer to where applications are being put to use,” according to a report in The Wall Street Journal.
Also: Ubuntu Linux Core 24 is a leap forward for IoT and edge computing
The integration of AI “into edge computing further underscores its importance,” Vandana Singh, senior vice president at Schneider Electric, told ZDNET. “AI algorithms deployed at the edge enable devices to process data locally, make autonomous decisions, and respond in real-time without relying on centralized servers. This not only reduces latency but also enhances privacy and security by minimizing the need to transmit sensitive data over the network.”
Career opportunities
Careers in the corporate end of the edge include — but are not limited to — edge network engineer, edge IoT architect, edge software engineer, edge solutions architect, and edge security specialist, as spelled out by TechRepublic’s Kihara Kimachia. At the device or sensor level, career opportunities include embedded software developer, RTOS engineer, and firmware engineer.
This calls for skills that involve designing and building edge systems — which may differ from “mainline” skills seen in many corporate data centers. The distinction is borne from “a rethinking of the architecture around what you compute, and where, and what you store, and where,” said Verizon’s Zirkle. Working at the edge, he added, means learning to “apply the benefits of edge to your data environment and objectives. Edge compute keeps data close by, meaning it doesn’t need to go back and forth to far away cloud and data centers.”
Also: AI at the edge: 5G and the Internet of Things see fast times ahead
Such skills “are more specialized compared to traditional IT skills,” Wayne Carter, vice president of engineering at Couchbase, told ZDNET. “They combine aspects of network engineering, software development, and security protocols to handle the unique challenges of edge computing environments.”
Essential skills for developing edge systems “include proficiency in real-time data processing and an understanding of decentralized architectures,” Sturgeon Christie, CEO of Second Skin Audio, told ZDNET. “These differ significantly from traditional IT skills, which focus more on centralized data storage and processing. Developers venturing into edge technology need to be adept in areas like machine learning and security protocols that are tailored for local and autonomous operations, without constant central oversight.”
This also calls for the ability to “focus primarily on optimizing data interactions and processing at the edge of the network,” said Carter. “You need to design systems that handle intermittent connectivity and synchronize data efficiently between the edge and the cloud. You need proficiency in technologies that facilitate real-time data analysis and decision-making directly at the data source.”
Also: If you want a career in AI, start with these 5 steps
Core to edge skills is “expertise in networking and connectivity, which ensures “seamless communication between edge devices and central systems,” Singh said. Importantly, she added, “edge systems often involve deploying solutions in remote or harsh environments, which can require various degrees of reliability and resilience while maintaining adaptability. Because of these off-site locations, the ability to remotely manage these assets needs to be top of mind during the design process.”
The professional working at the edge opens up new vistas for organizations, as “all of a sudden, applications or capabilities that require real-time or near real-time actions are possible,” Zirkle said. “You can take mission-critical steps in time-sensitive scenarios. You have data-intensive and time-sensitive considerations cared for at the same time. Imagine what can be done with that efficiency.”
