Computer vision in machine learning has allowed us to tell if there are accidents on roads or unsafe work environments and can be used in conjunction with complex systems such as smart sales assistants. Computer vision has opened up many possibilities in IoT. Computer vision is also one of the most challenging from a cost perspective. In the next two recipes, we are going to discuss two different ways of using computer vision. The first one takes in large amounts of images generated from IoT devices and performs predictions and analysis on them using the high-performance distributed Databricks format. In the next recipe, we are going to use a technique for performing machine learning on edge devices with a small amount of compute using a low compute algorithm.
Artificial Intelligence for IoT Cookbook
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Artificial Intelligence for IoT Cookbook
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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)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
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