Shipping Machine Learning Systems - A Practical Guide to Building, Deploying, and Scaling in Production

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.

List of contents

Preface; Introduction; Part I. Ready, Aim, Fire, Aim, Fire, ...: 1. Planning; 2. Data; 3. Model development; 4. Model deployment and beyond; 5. Compute optimizations; Part II. Case Studies: 6. Nauto: data and model management; 7. Kavak: ML serverless architecture for car sales; 8. Instacart: journey in building Griffin; 9. WhatsApp: enhancing ML operations for fraud and abuse detection model; 10. ShortlyAI: Your AI writing partner; References; Index.

About the author

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).

Product details

• Authors: Mohamed El-Geish, Mohamed (Monta Ai) Patel El-Geish, Shabaz Patel, Anand Sampat
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Release: 31.12.2025
• EAN: 9781009124201
• ISBN: 978-1-0-0912420-1
• No. of pages: 463
• Assisted by: Hira Dangol
• Subjects: Social sciences, law, business > Business > Management; machine learning, BUSINESS & ECONOMICS / Production & Operations Management, BUSINESS & ECONOMICS / Industries / Manufacturing, pattern recognition, Production & quality control management, Manufacturing industries, COMPUTERS / Data Science / Machine Learning, Business process / operations management