Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference

Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and InferenceAuthor: Menshawy, Ahmed Machine learning Published on 19 August 2025 by O'Reilly Media in the United States. Paperback softback 400 pages 177 x 233 x 21 636g 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,

Author: Menshawy, Ahmed

Machine learning

Published on 19 August 2025 by O'Reilly Media in the United States.

Paperback / softback | 400 pages
177 x 233 x 21 | 636g

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 applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process