Book Image

The Azure IoT Handbook

By : Dan Clark
Book Image

The Azure IoT Handbook

By: Dan Clark

Overview of this book

With the rise of cloud-based computing, deploying IoT systems has become more cost-effective for businesses. This transformation has led to developers and architects shouldering the responsibility of creating, managing, and securing these systems, even if they are new to the IoT technology. The Azure IoT Handbook is a comprehensive introduction to quickly bring you up to speed in this rapidly evolving landscape. Starting with the basic building blocks of any IoT system, this book guides you through mobile device management and data collection using an IoT hub. You’ll explore essential tools for system security and monitoring. Following data collection, you’ll delve into real-time data analytics using Azure Stream Analytics and view real-time streaming on a Power BI dashboard. Packed with real-world examples, this book covers common IoT use as well. By the end of this IoT book, you’ll know how to design and develop IoT solutions leveraging intelligent edge-to-cloud technologies implemented on Azure.
Table of Contents (18 chapters)
1
Part 1: Capturing Data from Remote Devices
7
Part 2: Processing the Data
12
Part 3: Processing the Data

Combining IoT with ML

Combining IoT with ML opens a wide range of innovative use cases across various industries. Here are some common and impactful use cases for IoT and ML integration:

  • Predictive maintenance: Sensors on machinery and equipment collect data on factors such as temperature, vibration, and wear. ML models analyze this data to predict when maintenance is needed, reducing downtime and preventing costly breakdowns.

    Industry: Manufacturing, energy, and transportation

  • Anomaly detection: IoT devices monitor data streams, such as patient vitals, financial transactions, or network traffic. ML algorithms detect anomalies and raise alerts for potential issues or security breaches.

    Industry: Healthcare, finance, and security

  • Smart agriculture: In the domain of agriculture, IoT sensors and cameras in fields gather data on soil conditions, weather, crop health, and animal behavior. ML models provide insights into optimal planting times, irrigation, and pest control...