Shipping Machine Learning Systems

Mohamed El-Geish

ISBN 10: 100912420X / ISBN 13: 9781009124201

Published by Cambridge University Press, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.

Seller Inventory # 9781009124201

Bibliographic Details

• Title: Shipping Machine Learning Systems
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 100912420X
• ISBN 13: 9781009124201
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 25D millimeters 229 millimeters width by 152 millimeters height by 25 millimeters depth
• Weight: 642 grams

About this title

Synopsis

This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.

About the Authors

Mohamed El-Geish is CTO and Co-Founder of Monta AI. He has built machine learning systems used daily by millions worldwide. He led Amazon's Alexa Speaker Recognition and Cisco's Contact Center AI, co-founded Voicea (acquired by Cisco), contributed to products at LinkedIn and Microsoft, and co-authored 'Computing with Data' (2019).

Shabaz Patel is Associate Director of Applied AI at Best Buy, where he architects scalable ML systems powering search and discovery experiences for millions of users. Previously, at One Concern, he spearheaded innovations in AI-driven climate risk mitigation. Educated at Stanford and IIT, he specializes in scalable MLOps and impactful AI deployments and founded Datmo, an ML startup.

Anand Sampat is Co-Founder and CTO, Overline AI. He is an ML Leader and serial entrepreneur. He previously co-founded Datmo (acquired by One Concern) and led ML Solutions for One Concern, led ML for New Products at PathAI, and led ML at SambaNova Systems.