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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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Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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Frequently bought together

Machine Learning for Streaming Data with Python

€35.99

Time Series Analysis with Python Cookbook

€38.99

Modern Time Series Forecasting with Python

€39.99

Total € 114.97

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Amazon verified reviews

Syeman Feb 21, 2023

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

Kim ly Oct 18, 2022

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 Customer Sep 28, 2022

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.

@maxgoff Aug 20, 2022

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.

Sonali Aug 30, 2022

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!

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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