For many scenarios, the cloud is used as a way to process data and apply business logic with nearly limitless scale. However, processing data in the cloud is not always the optimal way to run computational workloads: either because of connectivity issues, legal concerns, or because you need to respond in near-real time with processing at the Edge. In this session we dive into how Azure IoT Edge can help in this scenario. We will train a machine learning model in the cloud using the Microsoft AI Platform and deploy this model to an IoT Edge device using Azure IoT Hub. At the end, you will understand how to develop and deploy AI & Machine Learning workloads at the Edge.
The Internet of Things Event Learning Path is designed for Solution Architects, Business Decision Makers, and Development teams that are interested in building IoT Solutions with Azure Services. The content is comprised of 5 video based modules that approach 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 solutioning based on the Azure IoT reference architecture.
Each session includes a curated selection of associated modules from Microsoft Learn that can provide an interactive learning experience for the topics covered and may also contribute toward preparedness for the official AZ-220 IoT Developer Certification @ https://aka.ms/iotlp/certification.
This content may be reused as-is across partner, field, and third party events or modified to suit custom audiences. The video resources and presentation decks are open-source and can be found on GitHub @https://aka.ms/iotlp
To discuss this module and other videos in the IoT ELP series, visit http://aka.ms/iotlp/blog to start a conversation with the creators of this content!