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

Defining your analytics as a function

In order to get started with architecture, let's build an idea from the ground up using the different building blocks that are necessary to make this a minimal working product.

The first thing that you need to have for this is an understanding of the type of real-time analytics that you want to execute.

For now, let's go with the same example as in the previous chapter: a real-time business rule that prints an alert when the temperature or acidity of our production line is out of the acceptable limits.

In the previous chapter, this alert was coded as follows:

Code block 2-1

def super_simple_alert(datapoint):
  if datapoint['temperature'] < 10:
    print('this is a real time alert. temp too low')
  if datapoint['pH'] > 5.5:
    print('this...