Moxa's newly launched AIG-300 Collection IIoT gateways embody Azure IoT Edge integration, which provides vital worth within the type of distinctive options that assist within the creation of a cost- and time-efficient IIoT system. To allow seamless edge-to-cloud knowledge acquisition and system administration, IIoT gateways have to be extremely safe and dependable. The gateway is an answer for dependable, fast, and straightforward knowledge acquisition and system administration that's seamlessly built-in with Azure IoT Edge and powered by ThingsPro. With just a few easy configuration steps, knowledge collected from gadgets will be transferred to Azure IoT Edge after which to Azure IoT Hub. Then again, gadgets on the AIG sequence IIoT gateways will be successfully managed through the Azure IoT Hub to the Azure IoT Edge route.

Nvidia is launching its EGX Platform to bring real-time artificial intelligence to the edge of the network. This means AI computing will happen where sensors collect data before it is sent to cloud-connected datacenters. "There's a massive change in the computing industry being driven by growth of [internet of things] sensors," said Justin Boitano, senior director of enterprise and edge computing, in a press briefing. "There are cameras for seeing the world, microphones for hearing the world, and devices being deployed so machines can detect what is happening in the real world." But this also means there's an exponential increase in the amount of raw data that has to be analyzed.

The integration of Azure Machine Learning and Azure IoT Edge enables organizations and developers to apply AI and ML to data that can't make it to the cloud due to data sovereignty, privacy, and/or bandwidth issues. All models created using Azure Machine Learning can now be deployed to IoT gateways and devices with the Azure IoT Edge runtime. Models are operationalized as containers and can run on many types of hardware, from very small devices all the way to powerful servers. We're releasing this toolkit to help get you started with AI and Azure IoT Edge. The toolkit will show you how to package deep learning models in Azure IoT Edge-compatible Docker containers and expose those models as REST APIs.

Computer vision at the intelligent edge is real and it is here! Access sample code to get started and watch a live demo on the Channel 9 IoT Show for inspiration. Thanks to a collaboration between NVIDIA and Microsoft, the NVIDIA Metropolis video analytics application framework, which runs on EGX, is now optimized to work with Microsoft Azure IoT Edge. The NVIDIA Metropolis framework includes the NVIDIA DeepStream software developer kit. With Azure IoT Edge and NVIDIA DeepStream, you can take a small, inexpensive NVIDIA Jetson Nano Developer Kit and analyze HD video streams in real-time.

Today at the Microsoft Build developer conference, we are announcing a partnership with Qualcomm, one of the largest mobile and IoT chipset manufacturers in the world, to jointly create a vision AI developer kit. This will empower Qualcomm's latest AI hardware accelerators to deliver real-time AI on devices without the need for constant connectivity to the cloud or expensive machines. This vision AI developer kit brings all the key hardware and software required to develop camera-based IoT solutions using Azure IoT Edge and Azure Machine Learning (ML) – helping innovators deliver the next generation of AI-enabled robotics, industrial safety, retail, home and enterprise security cameras, smart home devices and more. This is a crucial step toward enabling developers to easily create, manage and monitor AI on the edge. This partnership allows developers to start building AI offerings with prebuilt solutions -- including customizable models -- or create new AI models and deploy directly to the cloud or to the new hardware accelerated devices.

This blog post is co-authored by Emmanuel Bertrand, Senior Program Manager, Azure IoT. We recently announced Azure Cognitive Services in containers for Computer Vision, Face, Text Analytics, and Language Understanding. You can read more about Azure Cognitive Services containers in this blog, "Brining AI to the edge." Today, we are happy to announce the support for running Azure Cognitive Services containers for Text Analytics and Language Understanding containers on edge devices with Azure IoT Edge. This means that all your workloads can be run locally where your data is being generated while keeping the simplicity of the cloud to manage them remotely, securely and at scale.

Microsoft's new Azure edge computing offerings are helping customers extend the reach of its cloud-based machine learning services, according to Clayton Fernandez, the company's global director, Internet of Things. In June this year, Azure IoT Edge hit general availability, offering customers of Microsoft's cloud service new capabilities including support for the Moby container management system at the edge, the Azure IoT Edge security manager, an Automatic Device Management (ADM) service, and a range of tools for developers. Coinciding with the announcement that it had been GAed, Microsoft open sourced the IoT Edge code and posted it on GitHub. "We started with putting a PC on everyone's desk," Fernandez told Computerworld. "Next came everyone having smartphones in their pockets -- but now things are going to start getting really interesting with this whole universe of interconnected devices that are coming together in the intelligent cloud. "Today we're accepting billions of signals securely; we ingest, we reason, we take action – we call it the intelligent edge because these devices are becoming so capable that they're helping power some of the most advanced algorithms.

Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.

ODMs can choose to harden the platform through Hardware Security Modules (HSM). Microsoft has made it easy to run machine learning models at the edge. Each model responsible for inferencing can be packaged and deployed as a standard module. Developers can train their models on Azure through Data Science VMs or Azure ML Studio. Azure IoT Edge also supports running models exported from Azure's AutoML services such as custom vision. Since each model is just a container/module, new models can be quickly pushed to the edge. With Microsoft's investment in ONNX, ML models built using different frameworks may be exported to a standard format before using them for inference. Azure IoT Edge plays a crucial role in Microsoft's vision of delivering Intelligent Cloud and Intelligent Edge.

Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.