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Edge AI solutions and applications vary from smartwatches to production lines and from logistics to smart buildings and cities. Most organizations are inclined towards edge AI because of its easy and varied applications. Here are the top 10 edge AI trends that would take place in 2022.
While edge computing is rapidly becoming a must-have for many businesses, deployments remain in the early stages. To move to production, edge AI management will become the responsibility of IT departments. To address the edge computing challenges related to manageability, security, and scale, IT departments will turn to cloud-native technology.
Computer vision has dominated AI deployments at the edge. Image recognition led the way in AI training, resulting in a robust ecosystem of computer vision applications. NVIDIA Metropolis, an application framework and set of developer tools that help create computer vision AI applications, has grown its partner network 100-fold since 2017 to now include 1,000+ members.
The intelligent factory is another space being driven by new edge AI applications. Factories can add AI applications onto cameras and other sensors for inspection and predictive maintenance. However, detection is just stepped one. Once an issue is detected, action must be taken. AI applications are able to detect an anomaly or defect and then alert a human to intervene. But for safety applications and other use cases when instant action is required, real-time responses are made possible by connecting the AI inference application with the IoT platforms that manage the assembly lines, robotic arms, or pick-and-place machines. Integration between such applications relies on custom development work. Hence, expect more partnerships between AI and traditional IoT management platforms that simplify the adoption of edge AI in industrial environments.
AI-on-5G combined computing infrastructure provides a high-performance and secure connectivity fabric to integrate sensors, computing platforms, and AI applications — whether in the field, on-premises, or in the cloud. Key benefits include ultra-low latency in non-wired environments, guaranteed quality-of-service, and improved security.
For organizations deploying edge AI, MLOps will become key to helping drive the flow of data to and from the edge. Ingesting new, interesting data or insights from the edge, retraining models, testing applications, and then redeploying those to the edge improves model accuracy and results. With traditional software, updates may happen on a quarterly or annual basis, but AI gains significantly from a continuous cycle of updates.
Increase in Edge Data Centers
One of the top Edge AI trends in 2022 is the increase in edge data centers across the world. The Edge AI prediction is that more than five million servers will be deployed at the edge by 2024. These edge data centers will increase in number because of the 5G network, IoT proliferation, data gap, SDN, and NFV tech, as well as video streaming with augmented reality and virtual reality. The demand is higher owing to its low latency, overcoming the intermittent connectivity and data storage near the end-user.
The process sequence needs completion within milliseconds, from the user terminal service to the display of 3D video. It is required o avoid VR sickness in VR applications that produce 3D video in real-time. Edge computing can even remotely provide low-latency high-quality VR.
No one likes to see advertisements endlessly, but isn't it a little more enticing when the advertising is for good? Based on real-time targeting data, edge computing will allow a programmatic Digital out of Home (DOOH) marketplace. In this, the advertising ecosystem connects to serve advertisements. AI further segments the audience's demographics and sentiment in front of the camera, allowing advertisers to become smart. Change of ads is one of the most used edge computing trends of 2020.
The move to edge computing would involve streamlined end-to-end client procedures from development to implementation at the edge. It is called edge ops. This will allow developers of all stripes to efficiently use the edge's power by improving their current applications. This also creates a whole new class of native edge applications.
In different industries, digital twin computing takes digital twins of things and humans and conducts computations in any desired combination. It needs wide bandwidth with thousands of sensors that relay data to update the digital twin. The digital twin can transmit a smaller subset of data and edge computing will do some of the on-site processing required.
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