diarizationThe predictive/prescriptive AI life cycle of a device starts with data collection design. Data is analyzed for factors such as correlation and variance. Then the devices start being manufactured. Other than a small number of sample devices, there is usually no device failures, that produce machine learning models. To compensate for this, most manufacturers use duty cycle thresholds to determine whether a device is in a good state or a bad state. These duty cycle standards may be that that the device is running too hot or an arbitrary value is put on a sensor for an alert. But the data quickly needs more advanced analysis. The sheer volume of data can be daunting for an individual. The analyst needs to look through millions of records to find the proverbial needle in a haystack. Using an analyst-in-the-middle approach using anomaly detection can...
Artificial Intelligence for IoT Cookbook
By :
Artificial Intelligence for IoT Cookbook
By:
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.
Related Content you might be interested in
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