Building Real-World ML Systems: The Practical Guide to Scalable, Reliable, and Maintainable AI
Turn Your Machine Learning Models into Scalable, Reliable Systems That Actually Deliver Results
Most machine learning books teach you how to build models. Very few teach you how to ship them. Even fewer show you how to build systems that last, scale confidently, and handle the chaos of real-world data and changing business needs.
Building Real-World ML Systems is your practical guide to everything that comes after training the model. If you've ever struggled with fragile pipelines, model drift, deployment nightmares, or infrastructure that can't keep up, this book speaks directly to your challenges.
You'll learn how to move from isolated experiments to production-grade systems using patterns, workflows, and architectures that have powered real ML deployments across industries. Whether you're a software engineer, data scientist, ML engineer, or tech lead, this book gives you the end-to-end thinking needed to ship robust ML systems that scale-without the guesswork.
Inside, you'll uncover:
How to design reliable pipelines for training, inference, and monitoringProven techniques to handle data versioning, labeling bottlenecks, and distribution shiftsDeployment workflows that reduce friction and improve reproducibilityScalable system designs using tools like Docker, Kubernetes, feature stores, and CI/CDStrategies for retraining, rollback, performance monitoring, and cost optimizationLessons from real-world failures-so you can avoid them before they hit youThis isn't another theory book. It's your playbook for ML in production-practical, actionable, and deeply grounded in the day-to-day realities of building machine learning at scale.
If you're serious about delivering machine learning systems that make an impact, this is the one book that belongs on your desk.
Build smarter. Ship faster. Scale fearlessly.
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