Remote locations. Rugged job sites. Spotty connectivity. In many industries, such conditions are a reality, making Internet of Things scenarios such as cloud analytics and real-time response more costly and unpredictable. Today Microsoft announced the public preview of Azure IoT Edge, a new service that deploys cloud intelligence to local IoT devices via containers.
We are also previewing new AI and advanced analytics tools that you can use at the edge, such as AI Toolkit for Azure IoT Edge, Azure Machine Learning, Azure Stream Analytics and Azure Functions. These tools will run on top of Azure IoT Edge, enabling real-time analytics and actions to happen closer to the source of the data. This is important for mission critical settings like hospitals or factories where a second delay could be detrimental.
At Microsoft Build in May, Microsoft’s director of Azure IoT, Sam George, demonstrated how Sandvik Cormorant — a Swedish manufacturer of industrial-grade, precision cutting tools — is using Azure IoT to help customers worldwide avoid catastrophic failures of its machinery, which exceeds $ 1 million in replacement costs.
Here’s what Magnus Ekbäck, Sandvik’s vice president of business development, said at the time about the benefits of Azure IoT Edge:
“We are streaming data from manufacturing machines, industrial equipment, pipelines and other remote devices connected to the Industrial Internet of Things. This creates a massive glut of operational data, which can be difficult — and expensive — to manage. By running the data through an analytics algorithm at the edge, inside a corporate network with Azure IoT Edge, we can set parameters on what information is worth sending to a cloud or on-premises data store for later use — and what isn’t.”
Added support for AI, Machine Learning, Stream Analytics and Functions on edge devices means companies will now be able to generate even greater insights into the state of their business operations. Support for Azure IoT Edge modules and runtime will enable companies to rapidly deploy apps and streamline management across their entire network. And Microsoft will continue working across the tech industry to make Azure IoT Edge a secure Intelligent Edge offering on any platform.
“Azure IoT Edge provided an easy way to package and deploy our machine learning applications. Traditionally, machine learning is something that has only run in the cloud, but for many IoT scenarios that isn’t good enough, because you want to run your application as close as possible to any events. Now we have the flexibility to run it in the cloud or at the edge — wherever we need it to be.” — Matt Boujonnier, analytics application architect for Schneider Electric