Azure Stack Edge solving AI problems at the edge
Published Jul 15 2020 09:08 AM 3,056 Views

AI and Machine Learning techniques are changing the ways industries process data. And one of the most exciting developments is the ability to process at the edge, next to cameras, sensors, or other systems generating that data. This allows you to get insights right away, without needing to send everything to the cloud first – saving you bandwidth and getting results faster. 


We designed Azure Stack Edge for exactly these situations. It’s an extension of the Azure cloud that lets you run analysis locally, but still controlled and managed from the cloud. So, you can deploy and monitor from the cloud, but have everything running at your site, right where your data is generated. Whether that’s a grocery store improving operations, a hospital improving efficiency in the Operating Room, or cities looking to improve traffic safety and efficiency. 


One of the cool things about Azure Stack Edge is you can install it, and then have it analyze data from your existing systems.  This opens up all sorts of new opportunities you might not expect. For example, airports have big existing scanners checking luggage for dangerous items. But as part of conservation efforts, they also want to check for illegal animal parts. This can reduce poaching and improve environment sustainability. With Azure Stack Edge you have those scanners do double duty. They can continue to do their normal work looking for dangerous items, and also send the scans to Azure Stack Edge to run AI models designed to detect other items. There’s a new Microsoft Mechanics video that highlights this use case and how it uses Azure Stack Edge as a platform to run locally. Definitely check out the video to get the full story. 


If your business is looking to do AI analysis at the edge, Azure Stack Edge is a great solution. It’s part of Azure, so there’s nothing to buy. You sign up like any other Azure service and we send you a server and bill you monthly on your normal Azure bill. It has built in ML acceleration hardware, and works with Microsoft’s container deployment systems. Read more about Azure Stack Edge here, or order one from the Azure Portal.

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‎Jul 15 2020 04:23 PM
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