The world has seen a rapid upsurge of innovation in smart devices at the edge of the network. From smartphones and sensors to drones, security cameras and wearable technologies, taking intelligence to the edge is creating a market that is anticipated to be worth well over $3 billion by 2025.
But it is not a market valuation driven purely by the devices. Rather it is the value of the opportunity created by a new generation of advanced applications that offer a new awareness and immediacy to events on the network.
Lower latency and higher speeds of connectivity offered by 4G – and, soon, more reliably and ubiquitous with 5G – mobile broadband, along with greater power and memory in a smaller footprint enables applications to number-crunch on devices that are independent and closer to the source.
With intelligence delivered on the spot, in real-time, AI at the edge allows mission-critical and time-sensitive decisions to be made faster, more securely and with greater specificity. For instance, AI-powered medical technologies can empower at-the-scene paramedics with immediate diagnostics data, while self-drive vehicles can be equipped to make micro-second decisions for safety.
Yet there are many scenarios where decisions still demand heavy computational lifting or where intelligence doesn’t need to be delivered in real-time. In these cases, AI-driven apps can comfortably remain located in the cloud, taking advantage of greater processing power and specialised hardware. For instance, hospital scans, machine analytics and drone inspections can happily accommodate data transfer lags to and from the cloud.
Whether considering deployment of AI at the edge or in the cloud – or perhaps a mix of both – there are three things you need to know:
 
1. ‘Trained’ versus ‘inference’ decisions in AI apps, and what this means for users
A key underlying factor to consider in application development for and deployment of AI

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