Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael 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)

How it works...

In this recipe, we added the libraries and then we cloned the Jetson inference repository. We then ran a series of make and linker tools to get the install working correctly. During this process, we downloaded a large set of pretrained models. We then started writing our code. Because the Jetson is limited in terms of its capabilities and memory, installing a full-featured IDE can be wasteful. One workaround for this is to use an IDE that supports SSH, such as Visual Studio Code, and remoting into the box via the IDE. You can then work with the device without tying up resources on the Jetson Nano.

To build out this project, first, we import the Jetson inference and utils libraries. In the previous recipes, we did a lot of the low-level work ourselves as far as using OpenCV to get the camera and then used other libraries to manipulate the images and draw bounding boxes. With Jetson's libraries, the vast majority of that code is handled for you. After we imported...