Shipping Machine Learning Systems

'I love when practitioners share their hard-earned wisdom. This book doesn't shy away from the messy realities of data work, from sourcing to compliance. The case studies are especially valuable, showing how their framework holds up in real-world use cases.' Chip Huyen, author of AI Engineering and Designing Machine Learning Systems 'This book by Mohamed El-Geish, Shabaz Patel, and Anand Sampat is an invaluable reference for engineers and managers building best-in-class ML and AI systems. It provides practical guidance on essential considerations, methods, and tools, enabling teams to confidently navigate the complexities of real-world AI development and deployment.' Hassan Sawaf, aiXplain 'Shipping Machine Learning Systems is the rare book that goes beyond algorithms to show what it really takes to build production ML systems. It combines clear explanations with honest discussions of trade-offs at every stage, grounded in real examples from industry leaders like Instacart and WhatsApp. An essential guide for anyone serious about shipping robust ML products.' Riham Selim, Meta 'There is a significant difference between developing a machine learning system in a controlled lab environment and deploying it in production to serve real users. This book bridges that critical gap with clarity and depth. It is an invaluable resource for machine learning practitioners and application developers seeking to bring cutting-edge ML systems into the real world - reliably, safely, and at scale.' Emad Elwany, AI Technology Executive 'Shipping machine learning systems is where theory meets the real world, and this book delivers the practical guidance every engineer needs to succeed. It covers the unglamorous but essential work of deploying, monitoring, and scaling models in production. Having built AI systems at Kolena, I found the lessons here refreshingly real and immediately useful. This is the book I would hand any team building serious ML products.' Mohamed Elgendy, Kolena