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 to do it...

The steps for this recipe are as follows:

  1. Go to the Azure portal where you created your custom vision project.
  1. Navigate your browser to https://customvision.ai and log in with your Azure credential. This will take you to the Projects page. There are some sample projects, but you will want to create your own. Click on the New project tile. Then, fill out the Create new project wizard. For this recipe, we are taking pictures of food and drink items so that we can use them in a workplace safety computer vision project. This type of computer vision could be used in an electronics shop, where people are eating in an environment with contaminants such as lead or carcinogens.
  2. On the main page of the project, you will see a Tags button. Click on the Untagged option (as shown in the following screenshot) and you will see all of the images that you uploaded:

  1. Click on the image and use the tools to draw a bounding box around the images. From here, you can draw bounding...