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Graph Machine Learning in Practice: Build Scalable GNN Pipelines with PyTorch & DGL
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Publisher: Independently Published
Published: Oct 23 2025
Pages: 222
Weight: 0.87
Height: 0.47 Width: 7.00 Depth: 10.00
Language: English
Graph Machine Learning in Practice: Build Scalable GNN Pipelines with PyTorch & DGL
How can machines truly understand relationships, not just between data points, but among people, systems, and events that define modern complexity? The answer lies in Graph Machine Learning, where structure meets intelligence.
This book takes you beyond theory to show how real-world graph systems are designed, optimized, and deployed. From fraud detection and recommendation engines to molecular discovery and enterprise analytics, you'll learn to build scalable Graph Neural Network (GNN) pipelines using PyTorch Geometric and Deep Graph Library (DGL), the two most powerful frameworks in modern graph AI engineering.
Written for machine learning engineers, data scientists, and AI enthusiasts who want more than academic examples, this hands-on guide turns complex graph theory into executable systems. Every chapter builds toward production-quality implementations, helping you master the full lifecycle of graph ML, from data ingestion to model explainability and governance.
You will learn how to:
Represent, preprocess, and sample graph data efficiently for large-scale training.
Build node, edge, and graph-level prediction models using GCNs, GraphSAGE, GAT, and advanced architectures.
Apply graph ML to real business problems: fraud detection, recommendations, molecular property prediction, and temporal analytics.
Scale training with distributed frameworks like DistDGL and GraphBolt.
Serve models through optimized inference pipelines, subgraph caching, and low-latency monitoring.
Implement explainability, fairness, and auditing mechanisms for production-ready trust and compliance.
Every concept is explained with clear reasoning and backed by complete, runnable code, no abstractions, no skipped steps. By blending the intuition of graph theory with the discipline of MLOps, this book shows how to build graph systems that perform reliably in the field, not just on paper.
Whether you're advancing an existing ML stack or starting your first graph project, Graph Machine Learning in Practice gives you the knowledge, patterns, and working templates to engineer GNN systems that scale, from prototype to production.
Take the next step toward mastering the most structural form of learning in AI.
Build smarter systems. Engineer connected intelligence. Get your copy today.
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