Scaling Graph Learning for the Enterprise (First Early Release)

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. Authors Ahmed Menshawy and Maraim Rizk Masoud from Mastercard's Cyber and Intelligence Division address core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

From their experience in 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.

You'll learn how to

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
Ahmed Menshawy is the Vice President of AI Engineering at Mastercard's Cyber and Intelligence and Maraim Rizk Masoud is a lead ML engineer.