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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

By : Roshak
4.9 (10)
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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

4.9 (10)
By: 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)
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Dealing with BOM changes

Bill of Materials (BOMs) are the components that make up the device. These can be resistors, chips, and other components. The life cycle of a typical IoT product is about 10 years. In that time, things can change with the product. A component manufacturer could discontinue a part such as a chip line. Outsourced manufacturers typically perform BOM optimization on a board layout, though BOM optimization can change the quality of the device. For example, it can change the sensitivity of the sensor or the lifetime of a device.

This can throw off trained models and can have a dramatic effect on any remaining useful life calculations and predictive maintenance models. When working with IoT and machine learning, tracking changes that have been made to any remaining useful life based on BOM and factory changes can help us detect issues with the quality and longevity of a device.

This is typically done with a database. When a device is made in a factory, that device...

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