Joos Korstanje - Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks
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- Book:Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks
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Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming
Key Features- Work on streaming use cases that are not taught in most data science courses
- Gain experience with state-of-the-art tools for streaming data
- Mitigate various challenges while handling streaming data
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.
You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.
By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
What you will learn- Understand the challenges and advantages of working with streaming data
- Develop real-time insights from streaming data
- Understand the implementation of streaming data with various use cases to boost your knowledge
- Develop a PCA alternative that can work on real-time data
- Explore best practices for handling streaming data that you absolutely need to remember
- Develop an API for real-time machine learning inference
This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
Table of Contents- An Introduction to Streaming Data
- Architectures for Streaming and Real-Time Machine Learning
- Data Analysis on Streaming Data
- Online Learning with River
- Online Anomaly Detection
- Online Classification
- Online Regression
- Reinforcement Learning
- Drift and Drift Detection
- Feature Transformation and Scaling
- Catastrophic Forgetting
- Conclusion and Best Practices
Joos Korstanje: author's other books
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Rapidly build practical online machine learning solutions using River and other top key frameworks
Joos Korstanje
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Dinesh Chaudhary
Content Development Editor: Joseph Sunil
Technical Editor: Rahul Limbachiya
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Marketing Coordinator: Shifa Ansari and Abeer Riyaz Dawe
First published: July 2022
Production reference: 1240622
Published by Packt Publishing Ltd.
Livery Place
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Birmingham
B3 2PB, UK.
ISBN 978-1-80324-836-3
www.packt.com
Joos Korstanje, with his master's degrees in both environmental sciences and data science, has been working on statistics and data science for almost 10 years. Through his work in different companies including Disney, AXA, and others, he has closely followed developments in data science and related fields. This experience in the business world has allowed him to write about data science from an applied point of view (through his books, Medium, Towards Data Science, LinkedIn, and more).
Olivia Petris is a big data engineer working as an IT consultant in a technology and advisory services firm based in Paris. On her professional journey, she's always looking for challenging and interesting assignments. Since her engineering diploma in computer science, she has chosen to be in the data science and big data field. Therefore, she continues to improve her skills and keep up to date with new IT and technology developments. In her free time, she enjoys traveling, practicing karate, and hanging out with her family and friends.
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