Book Image

Machine Learning for Streaming Data with Python

By : Joos Korstanje
Book Image

Machine Learning for Streaming Data with Python

By: Joos Korstanje

Overview of this book

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.
Table of Contents (17 chapters)
1
Part 1: Introduction and Core Concepts of Streaming Data
5
Part 2: Exploring Use Cases for Data Streaming
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
15
Chapter 12: Conclusion and Best Practices

Challenges of data preparation with streaming data

Before deep-diving into specific algorithms and solutions, let's first have a general discussion of why data preparation may be different when working with data that arrives in a streaming fashion. Multiple reasons can be identified, such as the following:

  • The first, obvious issue is data drift. As discussed in much detail in the previous chapter, trends and descriptive statistics of your data can slowly change over time due to data drift. If your feature engineering or data preparation processes are too dependent on your data following certain distributions, you may run into problems when data drift occurs. As many solutions for this have been proposed in the previous chapter, this topic will be left out of consideration in the current chapter.
  • The second issue is that population parameters are unknown. When observing data in a streaming fashion, it is possible, and even likely, that your estimates of the population...