Graph AI Systems Engineering: A Complete Guide to GraphRAG and Graph Machine Learning forScalable AI Systems with Python and PyTorch Geometric
English | May 2, 2026 | ASIN: B0GZCR72K1 | 322 pages | Epub | 7.71 MB
Graph AI Systems EngineeringA Complete Guide to GraphRAG, Graph Machine Learning, Knowledge Graphs, Graph Neural Networks, and LLM-Powered AI Systems with PyTorch Geometric, DGL, and Real-World Production ArchitecturesWhat if the most valuable signal in your data isn't the data itself-but the relationships between it?Traditional machine learning treats observations as isolated rows in a table. But real-world systems don't work that way. Fraud spreads through transaction networks. Recommendations emerge from patterns of user behavior. Supply chains, cybersecurity systems, financial markets, social platforms, molecular biology, and modern AI applications are fundamentally relational systems.Graph AI changes how machines understand these systems.In Graph AI Systems Engineering, you'll learn how to design, build, train, scale, and deploy production-grade graph intelligence systems using Graph Machine Learning, Knowledge Graphs, Graph Neural Networks (GNNs), Graph Transformers, GraphRAG pipelines, and LLM-powered architectures.This practical, engineering-focused guide is designed for developers, ML engineers, AI architects, data scientists, and technical professionals who want to move beyond isolated models and build intelligent systems that reason over relationships, structure, and context.Inside this book, you'll learn how to:Build graph data pipelines using PyTorch Geometric (PyG), DGL, and NetworkXDesign and train Graph Neural Networks including GCNs, GraphSAGE, GATs, GINs, and Relational GNNsWork with temporal and dynamic graphs for evolving real-world systemsCreate enterprise-grade knowledge graphs and GraphRAG pipelinesCombine LLMs with graph machine learning for semantic search, recommendation systems, and AI copilotsApply graph AI to fraud detection, healthcare, cybersecurity, logistics, NLP, and recommendation enginesScale graph AI systems using distributed training, neighborhood sampling, graph partitioning, and GPU accelerationDeploy production-ready graph applications with monitoring, inference pipelines, and MLOps workflowsGraphUnlike fragmented tutorials or shallow introductions, this book emphasizes both conceptual clarity and practical implementation. Every major topic is reinforced with visual explanations, architecture diagrams, mathematical intuition, code walkthroughs, troubleshooting guidance, production notes, and hands-on projects.You'll explore how companies like Google, LinkedIn, Pinterest, YouTube, Spotify, and major financial institutions use graph AI to power recommendation engines, fraud detection systems, ranking algorithms, and large-scale knowledge platforms.Inside the book, you'll build systems including:Fraud detection pipelines using transaction graphsGraph-powered recommendation systemsKnowledge graph reasoning applicationsTemporal graph forecasting modelsMolecular property prediction systemsLLM + GraphRAG hybrid architecturesEnterprise-scale graph ML workflowsProduction-ready graph inference pipelinesThe book follows a progressive roadmap:Foundation Track - Learn the core principles visually and intuitivelyPractitioner Track - Build graph ML systems step by stepProduction Track - Scale and deploy graph AI in real-world environmentsWhether you're an AI engineer, backend developer, data scientist, or researcher exploring next-generation AI systems, this book gives you the practical skills and architectural understanding needed to master Graph AI.The future of AI is not just language. It is relationships, structure, reasoning, and connected intelligence-and graphs are the language that makes it possible.
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