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Machine Learning for Streaming Data with Python

You're reading from   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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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781803248363
Length 258 pages
Edition 1st Edition
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Author (1):
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Joos Korstanje Joos Korstanje
Author Profile Icon Joos Korstanje
Joos Korstanje
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Introduction and Core Concepts of Streaming Data
2. Chapter 1: An Introduction to Streaming Data FREE CHAPTER 3. Chapter 2: Architectures for Streaming and Real-Time Machine Learning 4. Chapter 3: Data Analysis on Streaming Data 5. Part 2: Exploring Use Cases for Data Streaming
6. Chapter 4: Online Learning with River 7. Chapter 5: Online Anomaly Detection 8. Chapter 6: Online Classification 9. Chapter 7: Online Regression 10. Chapter 8: Reinforcement Learning 11. Part 3: Advanced Concepts and Best Practices around Streaming Data
12. Chapter 9: Drift and Drift Detection 13. Chapter 10: Feature Transformation and Scaling 14. Chapter 11: Catastrophic Forgetting 15. Chapter 12: Conclusion and Best Practices 16. Other Books You May Enjoy

Chapter 10: Feature Transformation and Scaling

In the previous chapter, you have seen how to manage drift and drift detection in streaming and online machine learning models. Drift detection, although not the main concept in machine learning, is a very important accessory aspect of machine learning in production.

Although many secondary topics are important in machine learning, some of the accessory topics are especially important with online models. Drift detection is particularly important, as the model's autonomy in relearning makes it slightly more black-box to the developer or data scientist. This has great advantages only as long as the retraining process is correctly managed by drift detection and comparable methods.

In this chapter, you will see another secondary machine learning topic that has important implications for online machine learning and streaming. Feature transformation and scaling are practices that are relatively well defined in traditional, batch machine...

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