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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 5: Online Anomaly Detection

Anomaly detection is a good starting point for machine learning on streaming data. As streaming data delivers a continuous stream of data points, use cases of monitoring live solutions are among the first that come to mind.

There are many domains in which monitoring is essential. In IT solutions, there is generally continuous logging of what happens in the systems, and those logs can be analyzed as streaming data.

In the Internet of Things (IoT), sensor data is being collected on sometimes a large number of sensors. This data is then analyzed and used in real time.

Real-time and online anomaly detection can be of great added value in such use cases by finding values that are far from the expected range of measurements, or otherwise unexpected. Detecting them on time can have great value.

In this chapter, you will first get an in-depth overview of anomaly detection and the theoretical considerations to take into account when implementing...

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