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)

Getting ready

To use Microsoft's custom vision service, you will require an Azure subscription. Then you will need to spin up a new custom vision project. There is a free tier for testing out small models and a paid tier for larger models and serving models at scale. After creating the custom vision project in the Azure portal, you will see two new projects in the resource group. The first will be for training, and the second will have a -prediction label appended to the name, which will be used for the predictions. 

Then you will require images of what you are classifying. In our case, we are identifying beverages in an environment with lead and carcinogen exposure. If you have completed the previous recipe, you will have a camera capturing images at 1 second intervals. To make an object detection model in cognitive services, you will need at least 30 images of each thing you are trying to classify. More images will improve accuracy. To get good accuracy, you...