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

Summary

In this chapter, you have first seen a general overview of classification and its use cases. You have understood how it is different from anomaly detection, but how it can sometimes still be applied to anomaly detection use cases.

You have learned about five models for online classification of which some are mainly adaptations of offline models, and others are specifically designed for working in an online manner. Both types exist, and it is important to have the tools to benchmark model performance before making a choice for a final model.

The model benchmark that you executed in Python was done in such a way as to find the best model in terms of the accuracy of the model on a test set. You have seen clear differences between the benchmarked models, and this is a great showcase for the importance of model benchmarking.

In the following chapter, you will do the same type of model benchmarking exercise, but this time, you will be focusing on a regression use case, which...