Overview
- Presents systematic comparison of OML and BML in terms of performance, time and memory requirements
- Explains how OML can be customized by hyperparameter tuning
- Accompanied with continuously-updated code and material in the GitHub repository
Part of the book series: Machine Learning: Foundations, Methodologies, and Applications (MLFMA)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications.
The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs.
OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice.
In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository (https://github.com/sn-code-inside/online-machine-learning). The repository is continuously maintained, so the notebooks may change over time.
Editors and Affiliations
About the editors
Eva Bartz is an expert in law and data protection. Within the wide area of data protection, she specializes particularly in the application of artificial intelligence and its benefits and dangers. Based on this vast experience,she founded Bartz & Bartz GmbH in 2014 together with Thomas Bartz-Beielstein and offers consulting for a variety of customers. She translates the academic expertise of Bartz & Bartz GmbH’s advisors - who are leading experts in their fields - into a benefit for her customers. One of these customers was the Federal Statistical Office of Germany (Destatis), and the study for them laid the groundwork for this book.
Bibliographic Information
Book Title: Online Machine Learning
Book Subtitle: A Practical Guide with Examples in Python
Editors: Eva Bartz, Thomas Bartz-Beielstein
Series Title: Machine Learning: Foundations, Methodologies, and Applications
DOI: https://doi.org/10.1007/978-981-99-7007-0
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-99-7006-3Published: 06 February 2024
Softcover ISBN: 978-981-99-7009-4Due: 15 April 2024
eBook ISBN: 978-981-99-7007-0Published: 05 February 2024
Series ISSN: 2730-9908
Series E-ISSN: 2730-9916
Edition Number: 1
Number of Pages: XIII, 155
Number of Illustrations: 11 b/w illustrations, 38 illustrations in colour
Topics: Artificial Intelligence, Machine Learning