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Machine Learning for Streaming Data with Python
Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks

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Machine Learning for Streaming Data with Python

Chapter 1: An Introduction to Streaming Data

Streaming analytics is one of the new hot topics in data science. It proposes an alternative framework to the more standard batch processing, in which we are no longer dealing with datasets on a fixed time of treatment, but rather we are handling every individual data point directly upon reception.

This new paradigm has important consequences for data engineering, as it requires much more robust and, particularly, much faster data ingestion pipelines. It also imposes a big change in data analytics and machine learning.

Until recently, machine learning and data analytics methods and algorithms were mainly designed to work on entire datasets. Now that streaming has become a hot topic, it becomes more and more common to see use cases in which entire datasets just do not exist anymore. When a continuous stream of data is being ingested into a data storage source, there is no natural moment to relaunch an analytics batch job.

Streaming analytics and streaming machine learning models are models that are designed to work specifically with streaming data sources. A part of the solution, for example, is in the updating. Streaming analytics and machine learning need to update all the time as new data is being received. When updating, you may also want to forget the much older data.

This and other problems that are introduced by moving from batch analytics to streaming analytics need a different approach to analytics and machine learning. This book will lay out the basis for getting you started with data analytics and machine learning on data that is received as a continuous stream.

In this first chapter, you'll get a more solid understanding of the differences between streaming and batch data. You'll see some example use cases that showcase the importance of working with streaming rather than converting back into batch. You'll also start working with a first Python example to get a feel for the type of work that you'll be doing throughout this book.

In later chapters, you'll see some more background notions on architecture and, then, you'll go into a number of data science and analytics use cases and how they can be adapted to the new streaming paradigm.

In this chapter, you will discover the following topics:

  • A short history of data science
  • Working with streaming data
  • Real-time data formats and importing an example dataset in Python

Technical requirements

You can find all the code for this book on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-for-Streaming-Data-with-Python. If you are not yet familiar with Git and GitHub, the easiest way to download the notebooks and code samples is the following:

  1. Go to the link of the repository.
  2. Go to the green Code button.
  3. Select Download ZIP:
Figure 1.1 – GitHub interface example

Figure 1.1 – GitHub interface example

When you download the ZIP file, you unzip it in your local environment, and you will be able to access the code through your preferred Python editor.

Setting up a Python environment

To follow along with this book, you can download the code in the repository and execute it using your preferred Python editor.

If you are not yet familiar with Python environments, I would advise you to check out Anaconda (https://www.anaconda.com/products/individual), which comes with the Jupyter Notebook and JupyterLab, which are both great for executing notebooks. It also comes with Spyder and VSCode for editing scripts and programs.

If you have difficulty installing Python or the associated programs on your machine, you can check out Google Colab (https://colab.research.google.com/) or Kaggle Notebooks (https://www.kaggle.com/code), which both allow you to run Python code in online notebooks for free, without any setup to do.

Note

The code in the book will generally use Colab and Kaggle Notebooks with Python version 3.7.13 and you can set up your own environment to mimic this.

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Work on streaming use cases that are not taught in most data science courses
  • Gain experience with state-of-the-art tools for streaming data
  • Mitigate various challenges while handling streaming data

Description

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.

Who is this book for?

This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.

What you will learn

  • Understand the challenges and advantages of working with streaming data
  • Develop real-time insights from streaming data
  • Understand the implementation of streaming data with various use cases to boost your knowledge
  • Develop a PCA alternative that can work on real-time data
  • Explore best practices for handling streaming data that you absolutely need to remember
  • Develop an API for real-time machine learning inference
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Table of Contents

16 Chapters
Part 1: Introduction and Core Concepts of Streaming Data Chevron down icon Chevron up icon
Chapter 1: An Introduction to Streaming Data Chevron down icon Chevron up icon
Chapter 2: Architectures for Streaming and Real-Time Machine Learning Chevron down icon Chevron up icon
Chapter 3: Data Analysis on Streaming Data Chevron down icon Chevron up icon
Part 2: Exploring Use Cases for Data Streaming Chevron down icon Chevron up icon
Chapter 4: Online Learning with River Chevron down icon Chevron up icon
Chapter 5: Online Anomaly Detection Chevron down icon Chevron up icon
Chapter 6: Online Classification Chevron down icon Chevron up icon
Chapter 7: Online Regression Chevron down icon Chevron up icon
Chapter 8: Reinforcement Learning Chevron down icon Chevron up icon
Part 3: Advanced Concepts and Best Practices around Streaming Data Chevron down icon Chevron up icon
Chapter 9: Drift and Drift Detection Chevron down icon Chevron up icon
Chapter 10: Feature Transformation and Scaling Chevron down icon Chevron up icon
Chapter 11: Catastrophic Forgetting Chevron down icon Chevron up icon
Chapter 12: Conclusion and Best Practices Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(9 Ratings)
5 star 55.6%
4 star 33.3%
3 star 0%
2 star 0%
1 star 11.1%
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Syeman Feb 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is well organized and provides important concepts for working with streaming data for use in machine learning. An aspect I like about it is the exposure to tools to be used for different parts of the process.
Amazon Verified review Amazon
Kim ly Oct 18, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have been working on big data analysis, especial streaming data, this book have saved me so much times to watch tutorial, The Author has provided a lot of coding example that I can learn and apply for my project. More than that, this book also very useful to explain the complex terminology or concept about big data. Highly Recommend.
Amazon Verified review Amazon
Amazon Customer Sep 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is about stream data machine learning using Python library River. The stream ML is different from regular ML.The book discusses a lot of applications using River, such as Online Anomaly Detection, Online Classification, Online Regression, Reinforcement Learning and Drift and Drift Detection, et al.It offers ready to use codes for the popular algorithms, OneClassSVM, Isolation Forest (HalfSpaceTrees), LogisticRegression, Perceptron(), RandomForest, ALMAClassifier, passive-aggressive (PA) classifier, LinearRegression, HoeffdingAdaptiveTreeRegressor, SGTRegressor, SRPRegressor.I like this book and I think it is a good book for the readers who want to learn stream data ML.
Amazon Verified review Amazon
@maxgoff Aug 20, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Review of Machine Learning for Streaming Data with Python(authored by Joos Korstanje)"Streaming viewership surpassed cable TV for the first time, says Nielsen”-- Headline from TechCrunch Article, 18 August 2022Data science is a calling.As Jennifer Shin, Senior Principal Data Scientist at Nielsen is quoted as saying:“’Possessed’ is probably the right word. I often tell people, ‘I don’t want to necessarily be a data scientist. You just kind of are a data scientist. You just can’t help but look at that data set and go, ‘I feel like I need to look deeper. I feel like that’s not the right fit.’”I think it’s interesting that I am writing this review of this particular book at this particular time, when Nielsen is reporting the (inevitable) ascendency of streaming viewership, (inevitably) surpassing that of cable. The trend in that direction has been clear for years now. And we hit that particular milestone just as Joos’ text is being published. Good timing, coincidence, dharma or part of the Great Universe’s Master Plan, the fact is, the knowledge from this text must be assimilated well and quickly by practitioners of the Art and Science of Machine Learning in production environments today.Streaming is the future of data processing. Especially with a doubling of IoT-connected devices over the next four years, each one generating real-time feeds, each device begging for immediate consumption of their data, Machine Learning for Streaming Data must be mastered by those of us, like Jennifer, who are possessed by this calling.If you haven’t used the River package in python, this book offers a very useful tutorial. River is a library to build online machine learning models using python. What’s an ‘online ML model?’ It’s a term meant to differentiate between more traditional approaches to ML, called offline learning.Offline learning is an approach that ingests all the data at one time to build a model whereas online learning is an approach that ingests data one observation at a time.Online ML models operate on data streams. But the concept of a data stream is a bit vague.In general, a data stream is a sequence of individual elements. In the case of machine learning, each element is a bunch of features. We call these samples, or observations. Each sample might follow a fixed structure and always contain the same features. But features can also appear and disappear over time, depending on the use case.Regardless of data source or use case, the River package can be very useful when it comes to ML for streaming data.I enjoyed digesting this book. If you write code and need to jump-start your understanding of ML for streaming data, this is the text for you. Joos’ book with associated code provides a quick introduction to the field with sufficient code examples to get you well on your way.
Amazon Verified review Amazon
Sonali Aug 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book nicely translates fundamentals of both classical Machine Learning using descriptive statistics as well as Deep Learning into its streaming counterpart. Streaming analytics is a lesser ventured area and not much research is available both from academia as well as industry. Given scarcity of resources on this topic, the author has done a great job in explaining existing Machine Learning algorithms using streaming context. The concept is nicely backed by coding examples which are easy to follow.In addition to Machine Learning concepts for streaming data, this book also discusses issues with data and best practices with streaming data as data drift. This is so important and often missed in productization of Machine Learning Models.And last but not the least, the book discusses in-depth on using reinforcement learning techniques for streaming data. This is again a novel concept and has many applications typically in the financial domain.Overall, I thoroughly enjoyed the book and am eager to apply some of the concepts discussed!
Amazon Verified review Amazon
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