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Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

By : Joos Korstanje
4.2 (9)
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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)
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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

Introducing model explicability

When models are learning in an online fashion, they are repeatedly relearning. This relearning process is happening automatically, and it is often impossible for a human user to keep an eye on the models continuously. In addition, this would go against the main goal of doing ML as the goal is to let machines—or models—take over, rather than having continuous human intervention.

When models learn or relearn, data scientists are generally faced with programmatic model-building interfaces. Imagine a random forest, in which hundreds of decision trees are acting at the same time to predict a target variable for a new observation. Even the task of printing out and looking at all those decisions would be a huge task.

Model explicability is a big topic in recent advances in ML. By throwing black-box models at data-science use cases, big mistakes are occurring. An example is that when self-driving cars were trained on a biased sample containing...

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Machine Learning for Streaming Data with Python
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