Data transfer slows down nearly every computing process. With traditional networks, data is transferred in raw, unprocessed formats, which means devices have to wait for bulky data packets to come across the network before beginning to work. Whereas processed data is a lot more compact, making it faster to transfer. What if the data sent across the network was already processed?
Edge AI allows data to be processed directly on devices or nearby local servers. This ability solves one of the biggest bottlenecks in AI — latency. Improving latency impacts performance and user experience for the better. However, there are more factors to make a case for Edge AI.
Factors Driving the Demand for Edge AI
Even the most routine applications use AI and machine learning algorithms. For example, think of how your music app predicts your likes based on previous listening history or how your email app decides which messages belong in the spam folder. Since more applications are using AI every day, organizations should consider the following needs emerging in the market:
- Improved data security. Edge AI allows businesses to provide users with AI-powered features while being more transparent about where data is being stored. Additionally, there is a constant influx of devices being connected to the network. Edge AI devices have the ability to provide additional security at the edge, keeping users protected in this age of devices.
- Faster transmission speed. AI, machine learning, and data analytics are a few examples of technologies that have high computing performance requirements. The speed of transmission within your network is a big contributor to latency. The ability to run algorithms at the edge means that data being pushed over the network is already processed and packaged for delivery — speeding up processes and putting less strain on the network.
- Balanced load sharing. Load balancing today is more complex because of the number of devices and their increasing capabilities. This means that simply balancing network loads between servers on a CDN is not enough. By leveraging computing power at the edge, businesses further alleviate the strain on their edge nodes and origin servers.
By 2026, the edge computing market is expected to grow 4-5 times larger than its state in 2020. This growth demonstrates that computing needs aren’t projected to slow down. Businesses must ask themselves how they can prepare their infrastructure to be ready for the edge AI needs over the next few years.
Building Your Edge AI Infrastructure
Edge AI is more complex than typical edge networks as it requires devices that can perform complex algorithms at the edge. Teams must ensure that edge hardware is capable of handling their specific workloads and that it will keep up when the local demands flux.
Your team is likely working hard to keep up with the user demands of your platform. That might be creating relevant content, adding new capabilities to applications, or extracting insights from ML algorithms. Our team can help you manage your edge AI hardware so that you can focus on what you do best. If you’d like to explore edge AI hardware options customized to your needs, let’s talk.