Machine Learning For Streaming Data With Python
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Practical Machine Learning for Streaming Data with Python
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more. You will: Understand machine learning with streaming data concepts Review incremental and online learning Develop models for detecting concept drift Explore techniques for classification, regression, and ensemble learning in streaming data contexts Apply best practices for debugging and validating machine learning models in streaming data context Get introduced to other open-source frameworks for handling streaming data.
Machine Learning for Streaming Data with Python
Author: Joos Korstanje
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-07-15
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 Book 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. 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 Who this book is 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.
The Pulse of Data: Real-Time Streaming Technologies Explained
Author: Deepak Venkatachalam
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-12-02
In today’s fast-paced digital economy, data is more than just an asset—it’s the fuel driving innovation and competitiveness. Yet, the sheer volume of information flooding into organizations presents a challenge: how can businesses harness this constant influx efficiently and effectively? Enter real-time data streaming, a revolutionary approach that processes information as it arrives, ensuring immediate insights and actions. Unlike traditional batch processing, where data is handled in large chunks at scheduled intervals, real-time streaming eliminates delays by analyzing each data point the moment it’s generated. This shift drastically reduces latency and enables businesses to make faster, more informed decisions. For senior executives and IT professionals, the implications are profound: enhanced decision-making capabilities, streamlined operations, and the ability to tap into new revenue streams—all in real time. This book serves as a vital resource, providing a foundational understanding of the critical differences between batch processing and streaming. It highlights how real-time data streaming empowers organizations to stay agile, responsive, and innovative. Through practical examples and insights, readers will explore the technologies and strategies that make real-time data an indispensable tool across industries. Whether you’re navigating the complexities of modern digital infrastructure or seeking to gain a competitive edge, this book offers essential guidance. By mastering the art of real-time data streaming, you’ll be equipped to drive operational efficiency, enhance customer experiences, and unlock new growth opportunities in an ever-evolving digital landscape.