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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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Getting ready

The dataset, along with the Databricks notebooks, is available in the GitHub repository. The dataset is unwieldy. It has bad columns with a high degree of correlation, which is another way of saying some sensors are duplicates, and there are unused columns and extraneous data. For the sake of readability, there will be two notebooks in the GitHub repository. The first does all of the data manipulation and puts the data into a data table. The second notebook does the machine learning. We will focus this recipe on the data manipulation notebook. At the end of the recipe, we will talk about two other notebooks to show an example of MLflow.

One other thing you will need in this recipe is an MLflow workspace. To set up an MLflow workspace, you will need to go into Databricks and create the workspace for this experiment. We will write the results of our experiment there.

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