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

Chapter 12: Conclusion and Best Practices

Throughout the chapters of this book, you have been introduced to the field of machine learning on streaming data, using mainly online models. In this last chapter, it is time for a recapitulative overview of all that has been seen throughout the eleven earlier chapters of the book.

This chapter will cover the following:

  • Best practices to keep in mind
  • Next steps for your learning journey
  • Best practices

Practice is always different from theory. You have seen a lot of theoretical knowledge throughout this book. In this final section, you will see a number of best practices that always need to be kept in mind while applying the theory in real-life use cases:

  1. Clean data/data quality

Data quality and problems with data understanding are daily problems in most companies. The famous saying goes: "Garbage in, garbage out," implying that when you do machine learning on garbage data, your outputs will...