Overview

Have you ever wondered how modern organizations train, scale, and deploy machine learning models on massive datasets? Are you struggling to move your models from local experiments to reliable, production-ready systems? Modern Machine Learning with Spark shows you how to build scalable AI solutions using Apache Spark, Spark MLlib, XGBoost, LightGBM, and modern MLOps practices. This book is designed to help you understand not just how distributed machine learning works, but how to apply it effectively in real-world environments. Inside, you'll learn how to: Process and analyze large datasets efficiently with Spark Understand when to use RDDs, DataFrames, and Datasets Scale gradient boosting, deep learning, NLP, and graph algorithms Build end-to-end ML pipelines with feature engineering and tuning Track experiments, deploy models, and monitor performance in production Rather than focusing on theory alone, this book emphasizes practical, hands-on examples that reflect real enterprise workflows. Whether you're a data scientist, ML engineer, software developer, or aspiring AI practitioner, you'll gain the skills needed to design machine learning systems that are scalable, maintainable, and production-ready. By the end of this book, you'll be able to confidently use Spark to power advanced machine learning workloads and transform experimental models into systems that perform reliably at scale. If you're ready to take your machine learning skills beyond prototypes and into real production environments, this book is your guide.

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