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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
About the author
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.
