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

Understanding ML basics

Although AI and ML are often used interchangeably, there is a distinction. AI refers to the simulation of human intelligence in machines or computer systems. It is a multidisciplinary field of computer science and engineering that aims to create intelligent agents or systems capable of perceiving their environment, reasoning, learning from experience, and making decisions to achieve specific goals.

Key components and concepts of AI include:

  • ML: ML is a subset of AI that focuses on the development of algorithms and models that allow machines to learn from data and make predictions or decisions without being explicitly programmed. It is a fundamental tool in AI research and applications.
  • Neural networks (NNs): NNs are computational models inspired by the human brain’s structure and functioning. Deep learning (DL), a subset of ML, utilizes deep NNs (DNNs) with multiple layers to handle complex tasks such as image and speech recognition.
  • ...