While the complexity of such designs can make edge devices expensive, the benefits are far beyond the associated costs.
In addition to real-time fast response, edge artificial intelligence has many significant advantages, such as higher security of the edge device itself and less data to and from the network. Edge artificial intelligence is very flexible because each application builds a customized solution. The inference function is preset in the edge device, so the requirements for operation and maintenance skills are relatively low.
In edge computing, developers can also move some complex operations to edge processors (such as routers, gateways, and servers) in the local network to distribute the computation across the network. Because data is also locally introduced and intelligence is also introduced locally, these edge processors have good operational reliability, which helps to deploy in areas where connections are intermittent or without network connectivity.
In general, solving challenges by building machine learning models is a very complicated matter. Developers must manage massive amounts of model training data, select the best algorithms that can be implemented, and manage cloud models for training models. The application developer then deploys the model to the production environment using a programming language such as Python. Smart edge device manufacturers will find it difficult to put resources into the artificial intelligence at the edge from scratch.
Indeed, artificial intelligence can make the already complex IoT space more complex, while edge artificial intelligence doubles the complexity of the Internet of Things. But with the right platform and partner support, developers can harness this complexity and achieve innovation that goes far beyond speech recognition and fingerprinting.