Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 1st Edition
by Chip Huyen
Why Read This Book?
- •Learn how to build ML systems that actually scale in production
- •Discover best practices for deploying, testing, and monitoring ML models
- •Understand the end-to-end lifecycle of machine learning systems
- •Gain insights from real-world industry case studies
- •Stay ahead in the fast-evolving field of AI & ML engineering
🚚 Expected delivery: 1–3 business days
"Designing Machine Learning Systems" by Chip Huyen is a must-have guide for anyone building scalable, production-ready ML applications. Whether you're a data scientist, ML engineer, or software developer, this book walks you through the end-to-end process of designing, deploying, and maintaining machine learning systems in the real world.
Chip Huyen, an expert in ML infrastructure and applied AI, blends practical case studies with actionable insights to teach you how to bridge the gap between model experimentation and production deployment. You’ll learn how to design resilient pipelines, monitor ML models, manage data, and optimize performance for real-world applications.
From handling concept drift and data versioning to implementing CI/CD for ML systems, this book provides step-by-step guidance that’s perfect for both beginners and experienced practitioners who want to master modern ML system design.
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