Scaling Graph Learning for the Enterprise

Scaling Graph Learning for the Enterprise: Building and Deploying Graph-Based AI Solutions

Architect your relational logic and master the high-performance protocols of modern graph engineering. Scaling Graph Learning for the Enterprise provides a definitive, engineering-first roadmap to the most significant shift in data connectivity since the move to NoSQL. Learn how to move beyond simple nodes to high-velocity, graph-native discovery—bridging the gap between a standalone data point and a sophisticated, multi-dimensional knowledge ecosystem—ensuring your technical projects are resilient, scalable, and ready for the 2026 global technology landscape.

Note: This is a digital product and the download link will be sent to your email address immediately after payment.

What You Will Learn:

Foundations of Graph Architecture: Master the core principles of Graph Neural Networks (GNNs) and the essential mechanics of how relational data drives modern AI.

Modern Enterprise Workflows: Step-by-step guidance on utilizing PyTorch Geometric (PyG) and Deep Graph Library (DGL) to maintain peak operational integrity in your models.

Scalable Learning Patterns: Discover how to utilize sampling techniques and distributed training to maintain the structural integrity and technical agility of your massive-scale graph datasets.

Strategic Security & Link Integrity: Learn advanced techniques for maintaining information security within your graph databases—including link prediction and anomaly detection—ensuring the technical agility of your fraud detection and recommendation systems.

Who This Book is For: This professional-grade guide is essential for Data Scientists, ML Engineers, and Data Architects. It is an invaluable resource for any technical lead—including those building highly connected, security-focused graduation projects like Smart Guard—aiming to master the structural integrity and technical agility required for modern, evidence-based software and system delivery.

Product Details:

Format: Digital PDF Download

Authors: Ahmed Menshawy; Sameh Mohamed; Maraim Rizk Masoud

Publisher: O'Reilly Media (September 2025)

ISBN-13: 9781098146023

ISBN-10: 1098146026