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 started to discover the field of architecture. You have built your own API on AWS, and you have seen the basic foundation of communication between systems. You should now understand that data is key in communication between systems and that good communication between systems is essential for delivering value through analytics.

This is especially true in the case of real-time and streaming analytics. The high speed and often large size of data can easily pose problems if architectural bottlenecks are not identified early enough in the project.

There are other topics that you must remember to take into account, including security, availability, and compliance. Those topics are best left to someone who makes it their full-time responsibility to take care of such data architecture problems.

In the following chapter, we'll go back to the core of this book, as you'll discover how to build analytics use cases on streaming data.