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Rapid Adoption of AI at the Edge with Azure

By Kyle Clubb | Posted on November 30, 2022 | Posted in AI/ML, IoT and Edge, Microsoft Azure

Global market research firm IDC estimates that more than half of all new IT infrastructure will be deployed at the edge by 2023. 

This growth is being fueled by a number of factors, including the unprecedented amount of data now available to enterprises and the ever-expanding market of internet of things (IoT) devices.

As edge computing has increased in popularity, the technology is being used in new and innovative ways. For example, the usage of artificial intelligence (AI).

This may seem counterintuitive. After all, AI is notorious for requiring a substantial amount of storage and compute in order to be effective—two things edge computing isn’t known for excelling at. But, as business cases for adopting AI continue to rise in number and edge devices continue to mature, cloud platforms like Azure are investing in edge solutions that make deploying AI away from traditional storage much easier.


The Vegas example

Casinos are renowned for their security measures. From individuals watching hands of blackjack in rafters above the casino floor, to an army of cameras monitoring every customer action, few industries are as devoted to maintaining a watchful eye.

As AI has matured as a technology, it’s increasingly being used for things like facial recognition. This usage is not without its controversy, but in the risk-averse environment of a casino, being able to identify a cheat or a customer that has been removed can go a long way toward stopping unexpected losses.

It also provides a perfect use case for adopting AI at the edge.

Facial recognition in casinos is all about speed and accuracy. The faster a bad actor can be identified, the more likely they will be escorted out the door before damage is done. 

The problem is, the sheer amount of data required by AI makes moving information to and from ground devices to, say, a centralized server, or even the cloud, a slow process. Latency, networking, processing data—each of these technical realities can limit the effectiveness of AI-powered facial recognition.

By adopting AI at the edge, edge devices like cameras can make the determination then and there, limiting the amount of lag that occurs from even the most sophisticated and high-bandwidth networks.

AI at the edge with Azure

As a platform, Azure offers a range of benefits for adopting AI at the edge.

First and foremost is Microsoft’s long-standing relationships with an array of OEMs, which makes integrating edge devices with Azure relatively easy.

Then there is Azure IoT Edge, which is designed to deploy and manage cloud-native workloads like AI, other Azure services, and even an organization’s own business logic on edge devices.

The service also helps streamline the process of updating and managing thousands of edge devices from Azure, ensuring every device is on par with the others when it comes to security and new features.


Getting started with AI at the edge

Given its increased popularity as a tool, it’s easy to forget that AI is still a relatively young technology—at least in the enterprise space.

It’s also rapidly changing both in capabilities and usage. Deploying AI at the edge is just the latest innovation, and as the technology matures, entire new avenues will be created for integrating AI within technology stacks and workflows.

If your organization is looking to get started with AI solutions, or would like to learn more about how the technology can be applied throughout your business, schedule some time with our experts