{"product_id":"online-learning-with-r-build-self-updating-machine-learning-models-with-real-time-data-and-continuous-prediction-9798253823200","title":"Online Learning with R: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous Prediction","description":"\u003cp\u003e • Author(s): Walton Bryant\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Data Modeling \u0026amp; Design\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eONLINE LEARNING WITH R 2026\u003c\/b\u003e\u003cb\u003e: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous Prediction\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eNo GPS? No problem. With this book, you'll always know what's ahead.\u003c\/p\u003e\u003cp\u003eMost machine learning books teach you how to build models that work once. This book shows you how to build systems that keep working no matter how fast the data changes.\u003c\/p\u003e\u003cp\u003eIn real-world environments, data never stops. User behavior shifts. Markets evolve. Sensors stream continuously. Static models fail quietly while decisions become increasingly wrong. The problem isn't your model, it's the assumption that the world stands still.\u003c\/p\u003e\u003cp\u003eThis book breaks that assumption.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eONLINE LEARNING WITH R 2026\u003c\/b\u003e is built for practitioners who want to move beyond batch workflows and design systems that learn continuously. It focuses on real-world implementation, not theory-showing how to build machine learning pipelines that update themselves, adapt to change, and remain reliable in production.\u003c\/p\u003e\u003cp\u003eInside, you'll learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cb\u003eBuild self-updating machine learning models that learn from streaming data\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eDesign real-time data pipelines that don't break under pressure\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eDetect and respond to concept drift before it destroys performance\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eEngineer features that remain consistent in live systems\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eDeploy low-latency prediction APIs using R\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eEvaluate models continuously without relying on outdated validation methods\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eBuild fault-tolerant systems that recover from failure automatically\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eApply online learning to time series, anomaly detection, and real-world forecasting\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eImplement complete end-to-end systems from ingestion to live deployment\u003c\/b\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis is not a theoretical guide. It is a system-building manual.\u003c\/p\u003e\u003cp\u003eEvery chapter is designed to reflect how machine learning actually operates in production continuous, imperfect, and constantly evolving. The examples focus on real use cases such as fraud detection, recommendation systems, predictive maintenance, and demand forecasting.\u003c\/p\u003e\u003cp\u003eIf you are a data scientist, machine learning engineer, or analyst ready to move beyond static models and build systems that adapt in real time, this book gives you the structure, tools, and mindset to do it.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47775535759511,"sku":"9798253823200","price":1721.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798253823200.webp?v=1777990958","url":"https:\/\/atlanticbooks.com\/products\/online-learning-with-r-build-self-updating-machine-learning-models-with-real-time-data-and-continuous-prediction-9798253823200","provider":"Atlantic Books","version":"1.0","type":"link"}