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.
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 within the associated module's folder in this repository.
To pull down a local copy of all included slide decks and video presentations, ensure that you have installed git, then clone this repo with:
git clone https://github.com/microsoft/Internet-of-Things-Event-Learning-Path.git
If you are interested in sharing or viewing the content right away, we have hosted the recordings on the IoT Developer YouTube where they can be viewed on-demand in a curated Internet of Things - Event Learning Path Playlist.
With 80% of the world's data collected in the last 2 years, it is estimated that there are currently 32 billion connected devices generating said data. Many organizations are looking to capitalize on this for the purposes of automation or estimation and require a starting point to do so. This session will share an IoT real world adoption scenario and how the team went about incorporating IoT Azure services.
Data collection by itself does not provide business values. IoT solutions must ingest, process, make decisions, and take actions to create value. This module focuses on data acquisition, data ingestion, and the data processing aspect of IoT solutions to maximize value from data.
As a device developer, you will learn about message types, approaches to serializing messages, the value of metadata and IoT Plug and Play to streamline data processing on the edge or in the cloud.
As a solution architect, you will learn about approaches to stream processing on the edge or in the cloud with Azure Stream Analytics, selecting the right storage based on the volume and value of data to balance performance and costs, as well as an introduction to IoT reporting with PowerBI.
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.
A large part of value provided from IoT deployments comes from data. However, getting this data into the existing data landscape is often overlooked. In this session, we will start by introducing what are the existing Big Data Solutions that can be part of your data landscape. We will then look at how you can easily ingest IoT Data within traditional BI systems like Data warehouses or in Big Data stores like data lakes. When our data is ingested, we see how your data analysts can gain new insights on your existing data by augmenting your PowerBI reports with IoT Data. Looking back at historical data with a new angle is a common scenario. Finally, we'll see how to run real-time analytics on IoT Data to power real time dashboards or take actions with Azure Stream Analytics and Logic Apps. By the end of the presentation, you'll have an understanding of all the related data components of the IoT reference architecture.
In this session we will explore strategies for secure IoT device connectivity in real-world edge environments, specifically how use of the Azure IoT Edge Gateway can accommodate offline, intermittent, legacy environments by means of Gateway configuration patterns. We will then look at implementations of Artificial Intelligence at the Edge in a variety of business verticals, by adapting a common IoT reference architecture to accommodate specific business needs. Finally, we will conclude with techniques for implementing artificial intelligence at the edge to support an Intelligent Video Analytics solution, by walking through a project which integrates Azure IoT Edge with an NVIDIA DeepStream SDK module and a custom object detection model built using CustomVision.AI to create an end-to-end solution that allows for visualization of object detection telemetry in Azure services like Time Series Insights and PowerBI.
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