Scaling Graph Learning for the Enterprise, 9781098146061
Paperback
Build enterprise-ready graph learning pipelines, from data to real-time inference.
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Scaling Graph Learning for the Enterprise

production-ready graph learning and inference

$162.10

  • Paperback

    400 pages

  • Release Date

    31 August 2025

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Summary

Unlock Enterprise Insights: A Practical Guide to Scaling Graph Learning

Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You’ll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

Drawing on thei…

Book Details

ISBN-13:9781098146061
ISBN-10:1098146069
Author:Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud
Publisher:O'Reilly Media
Imprint:O'Reilly Media
Format:Paperback
Number of Pages:400
Release Date:31 August 2025
Weight:0g
Dimensions:232mm x 178mm
About The Author

Ahmed Menshawy

Ahmed Menshawy is the Vice President of AI Engineering at Mastercard’s Cyber and Intelligence. In this role, he leads the AI Engineering team, driving the development and operationalization of AI products and addressing the broad range of challenges and technical debts surrounding ML pipelines. Ahmed also leads a team dedicated to creating a number of AI accelerators and capabilities, including Serving engines and Feature stores, aimed at enhancing various aspects of AI engineering. Ahmed is the coauthor of Deep Learning with TensorFlow and the author of Deep Learning by Example, focusing on advanced topics in deep learning.

Sameh is an expert in machine learning and health informatics. He has more than a decade of both academic and industrial experience in machine learning and artificial intelligence solutions. He obtained his PhD from the University of Galway, where he did research on machine learning on graphs and its applications in biomedical applications and a master’s degree in cardiovascular intervention medicine. He later worked for Mastercard, Carelon, and Microsoft in technical leadership roles where he built machine learning powered solutions in the domains of finance, healthcare insurance, and content generation. His contributions are mainly focused on the topics of representation learning, natural language processing, and health informatics.

Maraim Rizk Masoud is a leading ML engineer at Mastercard’s Cyber and Intelligence division, concurrently serving as an AI researcher. With a diverse background spanning both industry and academia, Maraim has delved into various AI domains, including natural language processing and AI governance. She holds an MSc in Machine Learning from Imperial College London and an MEng from the University of Southampton.

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