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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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Hopping

Hopping windows are tumbling windows that overlap. They allow you to set specific commands and conditions, such as every 5 minutes, give me the count of the sensor readings over the last 10 minutes. To make a hopping window the same as a tumbling window, you would make the hop size the same as the window size, as shown in the following diagram:

Stream Analytics

The following Stream Analytics example shows a count of messages over a 10-minute window. This count happens every 5 minutes:

SELECT EventTime, Count(*) AS Count
FROM DeviceStream TIMESTAMP BY CreatedAt
GROUP by EventTime, HopingWindow(minuites, 10, 5)

Spark

In PySpark, this would be done through a window function. The following example shows a Spark DataFrame that is windowed, producing an entry in a new entry in a DataFrame for every 5 minutes spanning a 10-minute period:

from pyspark.sql.functions import * 
windowedDF
= eventsDF.groupBy(window("eventTime", "10 minute", "5 minute")).count()
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