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

Understanding microservices architecture

The concept of microservices is important to understand when working on architectures. Although there are other ways to architecture software projects, microservices are quite popular for a good reason. They help teams be flexible and effective, and help to keep software flexible and clearly structured.

The idea behind microservices is in the name: software is represented as many small services that operate individually. When looking at the overall architecture, each of the microservices is inside a small, black box with clearly defined inputs and outputs. Processes are put in place to call the right black box at the right time.

Microservice architecture is loosely coupled. This means that there is no fixed communication between the different microservices. Instead, each microservice can be called, or not called, by any other services or code.

If a change needs to be made to one of the microservices, the scope of the change is fairly...