At Microsoft Build 2017 we shared our vision for Azure IoT Edge. At that time we demonstrated the idea of what Azure IoT Edge could do with Azure Stream Analytics job and Azure Machine Learning workloads by moving from Azure cloud to an IoT Edge device so that decisions could be made quickly and reliably without having to rely on the public Internet. This allowed million-dollar machines to operate reliably and safely with Azure IoT Edge, making intelligent decisions based on data from the machine. Deployment of these workloads can now be at a scale of thousands or millions of devices.
In November 2017, we shipped the public preview of Azure IoT Edge with the ability to move workloads from the Azure to Edge devices. These workloads included the AI Toolkit for Azure IoT Edge, Azure Machine Learning, Azure Stream Analytics, Azure Functions, and your own code. With these services on the edge, you could build rich and intelligent applications which operate locally, but can easily be deployed and managed from the Azure IoT Hub. We also articulated the security model for Azure IoT Edge and announced partnerships with NXP and Microchip for support of this model.
Read more about it in the Azure blog.