Getting started with IoT devices is not trivial. It usually involves lots of tooling, complex connectivity, and more. Ryan Winter from the Azure IoT Developers and Devices team is creating Getting Started Guide that will walk you through connecting a new device to Azure IoT using the Azure RTOS, and the Azure RTOS IoT middleware in under 30 minutes. Ryan will even demo one of these guides and will build Azure RTOS on an STMicroelectronics Discovery Kit and connect it to Azure IoT Central in about 5 minutes! Check out all the Getting Started Guide at https://aka.ms/gsg

IoT drives digital transformation, and among other things allows to ingest, aggregate and analyse industrial IoT data. But for the integration to work, Data scientists need to work with mechanical engineers, chemical engineers, robotics engineers, as well as IoT developers to build end to end solutions. Sudeep Gowrishankar and Sudhanshu Kashyap from Symphony AzimaAI join Olivier on the IoT Show to share their insights on how their company helps integrating Industrial AI with IoT data into end to end solutions for their customers leveraging Azure services. Learn more about Symphony AzimaAI at https://symphonyazimaai.com 00:00 Teaser 00:45 Introductions 07:17 Symphony AzimaAI presentation 10:16 Walkthrough typical Industrial AI solution architecture 12:31 The example of a Glass furnace operation optimization with an AI digital twin 19:47 Wrap up

IoT devices can work with cognitive services in the cloud for ML tasks such as face verification. However, it is often useful to have ML at the edge as well, to avoid streaming up data all the time – like a “wake word”. We will show you how Azure Sphere enables you to easily build ML at the edge that works with ML in the cloud, in the context of a face detection/recognition scenario. Learn more reading the blog post at https://aka.ms/iotshow/MLOnAzureSphere 00:00 Teaser 00:34 Introductions 01:34 Scenarios requiring running ML at the edge on Microcontrollers 03:25 Demo scenario and architecture 08:50 Use cases for using local ML in combination with Cloud ML 12:28 Security and privacy with Azure Sphere 14:58 Wrap up and call to action

Azure IoT Edge for Linux on Windows, also known as EFLOW, allows to run Linux-based cloud-native workloads on Windows IoT. This allows to retain existing Windows IoT investments and benefit from the power of Windows IoT for applications that require an interactive UX and high-performance hardware interaction. You no longer need to choose Windows or Linux; you can leverage the best of both platforms. Learn more about EFLOW at https://aka.ms/AzIoTEdgeForLinuxOnWindows 00:00 Teaser 00:31 Introductions 01:18 Announcing the preview availability of EFLOW 02:40 How does EFLOW work? 06:17 Demo: deploying Azure IoT Edge for Linux on a Windows device 18:33 Wrap up and call to action