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

Before using QnA Maker, you need a series of questions and answers. You can either point it at a website and have it parse the questions and answers or upload a TSV. For this recipe, we will use a TSV. There is a sample in the Git repo for this book.

To create a QnA Maker project, go to the QnA Maker portal at https://www.qnamaker.ai/ and click on Create a Knowledge Base. It will take you through a five-step wizard to create a QnA bot. The first step deploys the resources in Azure. The second step has you choose a language and associate the bot with the new service you just created. You will then give your project a name and upload the files with questions and answers.

The most straightforward way of adding questions and answers is to use a TSV. There are a few fields you will need. These are question, answer, source, and meta. meta and source are fields you can use to query data. For example, in our nutrition FAQ, we may have several different ways of understanding...