In cloud networking, and especially with IoT devices, edge computing has been a very important part of the network architecture. By having computing power at the edges of the network, data processing and storage is closer to the source of the data generation. By having the resources closer to the source, latency and bandwidth usage are greatly reduced. This improved speed enhances data analysis and decision-making.
Edge AI involves deploying AI algorithms and models directly on edge devices. In addition to the benefits of edge computing, edge AI can do real-time processing, which is very important for time-sensitive applications like smart home devices, autonomous vehicles, industrial automation, and even in retail experiences. Sounds great, right?
Well, hold on there... Gartner predicts that the use of embedded capabilities on edge devices, including those with advanced compute and AI functions, will steadily increase. As described in this Forbes article, unmanaged growth can greatly increase the complexity of dealing with all of these different devices.
Of course, a careful and managed integration of your edge devices can help limit any chaos introduced by such a complex and varied amount of data coming from the edge. Forbes also reports that we can expect increased adoption, improved and more varied performance from more advanced AI models, better interoperability, and a bigger focus on security.
Do you have any thoughts on how Edge AI will change the cloud and IoT landscape?
