finelybook
Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks
July 15 2022
Author: Joos Korstanje(Author)
Publisher Finelybook 出版社: Packt Publishing (July 15 2022)
Language 语言: English
pages 页数: 258 pages
ISBN-10 书号: 180324836X
ISBN-13 书号: 9781803248363
Book Description
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming
Key Features
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
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.
Author: the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
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
