Data Engineering for Machine Learning Designing Robust Pipelines and Workflows

Machine learning models are only as good as the data that fuels them. Data Engineering for Machine Learning offers a practical, hands-on guide to building the robust, scalable, and production-ready data pipelines that power successful AI systems. This book demystifies the core principles of data engineering while focusing on the specific needs of modern machine learning workflows.
From ingesting raw data to transforming it into high-quality features, you'll explore the essential tools and techniques for managing data at scale. With real-world examples, best practices, and step-by-step tutorials, this book equips you to design efficient workflows that serve both experimentation and production environments.
Whether you're building batch pipelines with Spark, managing real-time streams, or implementing feature stores and data validation systems, this book walks you through the critical steps needed to support machine learning at scale. It integrates cloud-native tools, CI/CD strategies, data contracts, and observability practices to help you ship reliable ML products faster. You'll also learn how to handle data drift, monitor quality, and enforce schema standards-ensuring your models remain trustworthy over time.
Key Features of This Book:End-to-end coverage of data engineering for ML workflowsHands-on code examples using Python, Airflow, Spark, Docker, and moreDeep dives into feature engineering, data versioning, and testingPractical guidance on building scalable, maintainable ML pipelinesReal-world case study on customer churn predictionThis book is ideal for machine learning engineers, data engineers, and software developers looking to strengthen their data pipeline expertise. It's also valuable for data scientists transitioning into production workflows or platform teams seeking to support ML efforts at scale.

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