Take a look at Azure Stack Edge and its growing application for AI and machine learning workloads. Find out how it’s being piloted, as part of Microsoft's AI For Good program, at London’s Heathrow Airport, to prevent illegal wildlife trafficking by inspecting passenger luggage for positive matches. All this is achieved using Azure Custom Vision AI models running locally on Azure Stack Edge. Matt McSpirit, Senior Program Manager for Azure Stack, joins Jeremy Chapman to share how the Azure AI Customer Engineering Team has been working with Heathrow to build a solution, combining a 3D scanner with AI models created in Azure, that cuts time and optimizes the process of early detection.  If you’re new to Azure Stack Edge, it is part of our Azure Stack family. When you're deploying workloads, such as machine learning to the Edge, and need to avoid the latency caused by roundtripping data to the cloud and back for processing, you can use Azure Stack Edge. This replaces compute power close to where you need it, so hardware accelerated data processing and AI inferencing can all be done locally. Now, management is performed remotely and centrally through Azure. Want to build out similar solutions customized to your specific needs? Watch to find out what the setup and management experience looks like, and what you need to deploy in order to run it all on your Azure Stack Edge device. 

For more tips like this, check out the working remotely playlist at www.youtube.com/FoetronAcademy . Also, if you need any further assistance then you can raise a support ticket and get it addressed.