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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

By : Roshak
4.9 (10)
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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

4.9 (10)
By: Roshak

Overview of this book

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease. By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)
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Getting ready

This recipe will be quite verbose in terms of the amount of code we'll be seeing, but everything will be done in Docker. You can use VS Code's Docker extension to work directly within the Docker container. You will also need a device with a webcam attached to it. This could be a laptop or a Raspberry Pi with a webcam – it doesn't really matter. For this recipe, we are going to set up a machine learning service, a camera streaming service, and a service that allows the devices to know where other devices are, and allow you to view your classification across your entire fleet of devices.

Although this is fairly simple, listing the code for all of the containers would take dozens of pages. For the sake of brevity, in this recipe, we are going to show the computer vision module. The rest of the modules can be run using Docker and the code in the GitHub repository for this book.

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