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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
Get in touch
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1
Part 1: Introduction and Core Concepts of Streaming Data
Part 1: Introduction and Core Concepts of Streaming Data
Free Chapter
2
Chapter 1: An Introduction to Streaming Data
Chapter 1: An Introduction to Streaming Data
Technical requirements
A short history of data science
Working with streaming data
Real-time data formats and importing an example dataset in Python
Summary
Further reading
3
Chapter 2: Architectures for Streaming and Real-Time Machine Learning
Chapter 2: Architectures for Streaming and Real-Time Machine Learning
Technical requirements
Defining your analytics as a function
Understanding microservices architecture
Communicating between services through APIs
Demystifying the HTTP protocol
Building a simple API on AWS
Big data tools for real time streaming
Summary
Further reading
4
Chapter 3: Data Analysis on Streaming Data
Chapter 3: Data Analysis on Streaming Data
Technical requirements
Descriptive statistics on streaming data
Introduction to sampling theory
Overview of the main descriptive statistics
Real-time visualizations
Building basic alerting systems
Summary
Further reading
5
Part 2: Exploring Use Cases for Data Streaming
Part 2: Exploring Use Cases for Data Streaming
6
Chapter 4: Online Learning with River
Chapter 4: Online Learning with River
Technical requirements
What is online machine learning?
Using River for online learning
Summary
Further reading
7
Chapter 5: Online Anomaly Detection
Chapter 5: Online Anomaly Detection
Technical requirements
Defining anomaly detection
Exploring use cases of anomaly detection
Comparing anomaly detection and imbalanced classification
Algorithms for detecting anomalies in River
Going further with anomaly detection
Summary
Further reading
8
Chapter 6: Online Classification
Chapter 6: Online Classification
Technical requirements
Defining classification
Identifying use cases of classification
Overview of classification algorithms in River
Summary
Further reading
9
Chapter 7: Online Regression
Chapter 7: Online Regression
Technical requirements
Defining regression
Use cases of regression
Overview of regression algorithms in River
Summary
Further reading
10
Chapter 8: Reinforcement Learning
Chapter 8: Reinforcement Learning
Technical requirements
Defining reinforcement learning
The main steps of a reinforcement learning model
Exploring Q-learning
Deep Q-learning
Using reinforcement learning for streaming data
Use cases of reinforcement learning
Implementing reinforcement learning in Python
Summary
Further reading
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
Part 3: Advanced Concepts and Best Practices around Streaming Data
12
Chapter 9: Drift and Drift Detection
Chapter 9: Drift and Drift Detection
Technical requirements
Defining drift
Introducing model explicability
Measuring drift
Measuring drift in Python
Counteracting drift
Summary
Further reading
13
Chapter 10: Feature Transformation and Scaling
Chapter 10: Feature Transformation and Scaling
Technical requirements
Challenges of data preparation with streaming data
Scaling data for streaming
Transforming features in a streaming context
Summary
Further reading
14
Chapter 11: Catastrophic Forgetting
Chapter 11: Catastrophic Forgetting
Technical requirements
Introducing catastrophic forgetting
Catastrophic forgetting in online models
Detecting catastrophic forgetting
Model explicability versus catastrophic forgetting
Summary
Further reading
15
Chapter 12: Conclusion and Best Practices
Chapter 12: Conclusion and Best Practices
Going further
Summary
Why subscribe?
16
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Implementing reinforcement learning in Python

Let's now move on to an example in which streaming data is used for Q-Learning. The data that we will be using is simulated data of stock prices:

  1. The data is generated in the following block of code.

The list of values that is first generated is a list of 30,000 consecutive values that represent stock prices. The data generating process is the starting point of 0 and at every time step, there is a random value added to this. The random normal values are centered around 0, which indicates that prices would go up or down by a step size based on a standard deviation of 1.

This process is often referred to as a random walk and it can go quite far up or down. After that, the values are standardized to be within a normal distribution again.

Code Block 8-1

import numpy as np
import matplotlib.pyplot as plt
import random
starting = 0
values = [starting]
for i in range(30000):
    values.append(values...
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End of Section 9
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