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 robust graph learning systems 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-preserving techniques to the graph learning process