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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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How it works...

To import the libraries on the Raspberry Pi you will need to log in to the Raspberry Pi and use pip to install azure-iot-device and SenseHat. Next, you'll need to go onto that machine and create a file called device.py. Then you will import the time, Azure IoT Hub, Sense HAT, and json libraries. Next, you'll need to go into IoT Hub and create a device through the portal, get your connection string, and enter it in the spot where it says Your device key here. You then initialize SenseHat and set the internal measuring units to True, initializing our sensors. Then create a helper function that combines our x, y, and z data. Next, get the data from sensors and send that to IoT Hub. Finally, wait for a second before sending that data again.

Next, go into the Stream Analytics job that you had set up and click on Edit query. From here, create a common table expression. A common table expression allows you to make a complex query more simple. Then use...

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