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...

After completing the wizard, you should see something in your Visual Studio Code explorer that looks like the following:

Let's explore what was created for you. The main entry point for the project is main.pymain.py has a sample to help make the development time faster. To deploy main.py, you will use the deployment.template.json file. Right-clicking on deployment.template.json brings up a menu that has an option to create a deployment manifest. In the modules folder, there is a sample module with three Docker files for ARM32, AMD64, and AMD64 in debug mode. These are the currently supported chip set architectures. Dockerfile.arm32v7 is the architecture that is supported on Raspberry Pi v3.

To make sure you build ARM32 containers and not AMD64 containers, go into the module.json file and remove any references to other Docker files. For example, the following has three Docker references:

platforms": {
"amd64&quot...