Predictive maintenance is one of the most sought after machine learning solutions for IoT. It is also one of the most elusive machine learning solutions for IoT. Other areas of machine learning can easily be solved, implementing Computer Vision, for example, can be done in hours using tools such as OpenCV or Keras. To be successful with predictive maintenance you first need the right sensors. The Data collection design recipe in Chapter 2, Handling Data, can be used to help determine proper sensor placement. The Exploratory factor analysts recipe in Chapter 2, Handling Data can help determine the cadence with which the data needs to be stored. One of the biggest hurdles to implementing predictive maintenance is that there needs to be a sufficient amount of device failures. For rugged industrial devices, this can take...
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.
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Current Title:
Artificial Intelligence for IoT Cookbook
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
Customer Reviews