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

Building basic alerting systems

In the previous parts of this chapter, you have seen an introduction to descriptive statistics and visualization.

Basic alerting systems will be covered as the last data analysis use case. In this part, you will see how you can use basic alerting systems on streaming data. For this, you will see how you can leverage descriptive statistics together with business rules to automatically generate alerts in real time. Example methods for alerting systems are as follows:

  • Alerting systems on extreme values
  • Alerting systems on process stability
  • Alerting systems on constant variability
  • Statistical process control and Lean Six Sigma control charts

Alerting systems on extreme values

The first example for alerting and monitoring systems on streaming data is the use case that you have seen in earlier chapters: coding a business rule that sends an alert once observed values are outside of hardcoded boundaries.

This example was coded...