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Apart from being highly responsive in real time, edge-based AI has significant advantages such as greater security built into the edge devices and lesser data flowing up and down the network. It is highly flexible, as customized solutions are built for each application. Since the inferences are pre-built into the edge devices, it needs fewer skills to operate and maintain.
Edge computing also allows developers to distribute computing across the network by transferring some sophisticated activities to edge processors in the local network like routers, gateways, and servers. They provide very good operational reliability as data is stored and intelligence is derived locally helping deployment in areas of intermittent connectivity or without a network connection.
Ordinarily, building a machine learning model to solve a challenge is complex. Developers have to manage vast amounts of data for model training, choose the best algorithm to implement, and manage the cloud services to train the model. Application developers then deploy the model into a production environment using programming languages like Python. The smart edge device manufacturer will find it extremely difficult to invest in resources to execute an AI implementation on edge from scratch.