Scaling Graph Learning for the Enterprise
Production-Ready Graph Learning and Inference
Published on 19. August 2025
Book
Paperback/Softback
400 pages
978-1-0981-4606-1 (ISBN)
Description
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 their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.
Understand the importance of graph learning for boosting enterprise-grade applications
Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines
Use traditional and advanced graph learning techniques to tackle graph use cases
Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications
Design and implement a graph learning algorithm using publicly available and syntactic data
Apply privacy-preserved techniques to the graph learning process
Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.
Understand the importance of graph learning for boosting enterprise-grade applications
Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines
Use traditional and advanced graph learning techniques to tackle graph use cases
Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications
Design and implement a graph learning algorithm using publicly available and syntactic data
Apply privacy-preserved techniques to the graph learning process
More details
Language
English
Place of publication
Sebastopol
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 233 mm
Width: 178 mm
Thickness: 19 mm
Weight
585 gr
ISBN-13
978-1-0981-4606-1 (9781098146061)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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 machine learning 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.