Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Machine Learning for Streaming Data with Python
  • Table Of Contents Toc
Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

By : Joos Korstanje
4.2 (9)
close
close
Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

4.2 (9)
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)
close
close
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

Using reinforcement learning for streaming data

As discussed throughout earlier chapters, the challenge of building models on streaming data is to find models that are able to learn incrementally and that are able to adapt in the case of model drift or data drift.

Reinforcement learning is a potential candidate that could respond well to those two challenges. After all, reinforcement learning has a feedback loop that allows it to change policy when many mistakes are made. It is therefore able to adapt itself in the event of changes.

Reinforcement learning can be seen as a subcase of online learning. At the same time, the second specificity of reinforcement learning is its focus on learning actions, whereas regular online models are focused on making accurate predictions.

The split between the two fields is present in practice in the types of use cases and domains of application, but many streaming use cases have the potential to benefit from reinforcement learning and it is...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Machine Learning for Streaming Data with Python
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon