See how the Azure IoT team is working with the Smart Cities ecosystem to enable interoperability by providing DTDL-based ontologies.

Common representation of places, infrastructure, and assets will be paramount for interoperability and enabling data sharing between multiple domains. It is our goal to provide a DTDL-based ontology definition to provide common ground for modeling environments leveraging well-established industry standards, accelerate developers time to results, and enable interoperability between DTDL-based solutions from different solution providers. In this episode we present the work we have been doing with our partners Sirus/ OASC to provide DTDL based Smart Cities ontology starting with ETSI CIM NGSI-LD models.

Learn more about the Smart Cities Ontology for Digital Twins: https://aka.ms/iotshow/SmartCitiesOntologyForDigitalTwins

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

See how Azure Sphere allows to run ML at the edge combined with Cloud AI in this demo on the IoT Show.

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

Microsoft IoT Developers’s Olivier Bloch shares his experience with an IoT Starter kit.

I was skeptical, but now stand corrected: you can connect an IoT Starter Kit designed for kids and beginners to Azure IoT Hub with no code, and it definitively is not that hard. See how that’s done with the M5Go IoT Starter Kit from M5Stack, based on an ESP32 chip and using Blocky.

Learn more reading the blog post at https://aka.ms/M5StackAzureIoTNoCodeBlog

Learn how to develop real-world Internet of Things solutions built with Microsoft Azure services from experts from around the world.

In this single-day event, we will cover topics ranging from IoT device connectivity, IoT data communication strategies, use of artificial intelligence at the edge, data processing considerations for IoT data, and IoT solutions based on the Azure IoT reference architecture. At the end of this event, you will have the knowledge to begin your journey to become a certified Azure IoT Developer!

Have you ever wondered how to build a workplace health & safety solution end-to-end?

Teo De Las Heras from the Azure IoT team joins us to demo and explain the architecture of such as solution built on Azure with IoT services.

Not only will you see the solution at work but you will also learn where you can find the entire solution for you to deploy on your own Azure subscription and use as a demo, or even as the starting point for your own solution.

Check it out on GitHub at https://aka.ms/iotshow/workplacesafety

Azure IoT Plug and Play is now natively supported in IoT Central. Solution builders can develop end-to-end IoT solutions using IoT Central and PnP ready devices with zero code.

This drastically reduces development and deployment costs and overall time to market. ISVs and partners building Line of Business (LOB) applications can rapidly accelerate development by using ready-to-use Plug and Play devices from a wide selection of devices from the Plug and Play certified catalog.

74% of IoT deployments slow down or stall completely as users grapple with the complexity of provisioning and managing their edge hardware at scale.

Learn how the integration of ZEDEDA’s IoT Edge orchestration solution with Azure IoT makes it possible to fast track and scale your entire project.

Features include one-click bulk provisioning, full lifecycle management of hardware, the Azure IoT Edge runtime, Azure IoT modules and any other installed apps, security enhancements like distributed firewall and concurrent support for legacy apps deployed in Windows VMs.

Check out Zededa Edge Quick Connect on the Azure Marketplace at https://aka.ms/iotshow/ZededaEdgeQuickConnect

You can now connect existing sensors to Azure with little to no-code using IoT Plug and Play bridge!

For developers who are building IoT solutions with existing hardware attached to a Linux or Windows gateway, the IoT Plug and Play bridge provides you an easy way to connect these devices to IoT Plug and Play compatible services.

Check out the GitHub repo for IoT Plug and Play bridge at https://aka.ms/IoTPlugAndPlayBridge

Building Vision AI applications has never been that simple with Azure IoT Central and IoT Edge.

Today, you can use various technologies to create Video Analytics solutions end to end, but assembling all these technologies from video acquisition, analytics at the edge to managing the cameras and gateways is not trivial.

The Azure IoT Central team just released a new App Template and IoT Edge modules that will help you do all this in a matter a hours.

Check out this demo heavy episode of the IoT Show with Nandakishor Basavanthappa, PM in the Azure IoT Central team.

Learn more reading the bog post at https://aka.ms/iotshow/VisionAIInIoTCentral

Get started today

  • You can use the new Video Analytics for Object & Motion Detection template to build and deploy your live video analytics solution.
  • You can build Video Analytics solution within hours by leveraging Azure IoT Central, Live Video Analytics, and Intel.
  • You can learn more about Live Video Analytics on IoT Edge here and try out some of the other video analytics scenarios via the quickstarts and tutorials here. These show you how you can leverage open source AI models such as those in the Open Model Zoo repository or YOLOv3, or custom models that you have built, to analyze live video.
  • You can learn more about the OpenVINO™ Inference server by Intel® in Azure marketplace and its underlying technologies here. You can access developer kits to learn how to accelerate edge workloads using Intel®-based accelerators CPUs, iGPUs, VPUs and FPGAs. You can select from a wide range of AI Models from Open Model Zoo