Introduction | Graph Neural Networks (GNNs) & Knowledge Graphs on AWS

The rapid advancements in artificial intelligence (AI) have unlocked new ways to process and learn from complex, interconnected data. While traditional deep learning models excel at structured and unstructured data, they struggle to capture relationships between entities. This is where Graph Neural Networks (GNNs) and Knowledge Graphs come in—offering a powerful way to model dependencies, enhance reasoning, and improve AI predictions. In this article series, we’ll explore how AWS provides scalable infrastructure for building, training, and deploying GNN-based AI models. From Amazon Neptune for knowledge graph storage to SageMaker for graph-based ML, we’ll dive into practical implementations and real-world use cases such as fraud detection, recommendation systems, and AI-powered search engines. This first article will introduce the core concepts of GNNs, why they matter, and how AWS enables scalable Graph AI. Let’s get started!

Mar 15, 2025 - 09:34

Introduction | Graph Neural Networks (GNNs) & Knowledge Graphs on AWS

The rapid advancements in artificial intelligence (AI) have unlocked new ways to process and learn from complex, interconnected data. While traditional deep learning models excel at structured and unstructured data, they struggle to capture relationships between entities. This is where Graph Neural Networks (GNNs) and Knowledge Graphs come in—offering a powerful way to model dependencies, enhance reasoning, and improve AI predictions.

In this article series, we’ll explore how AWS provides scalable infrastructure for building, training, and deploying GNN-based AI models. From Amazon Neptune for knowledge graph storage to SageMaker for graph-based ML, we’ll dive into practical implementations and real-world use cases such as fraud detection, recommendation systems, and AI-powered search engines.

This first article will introduce the core concepts of GNNs, why they matter, and how AWS enables scalable Graph AI. Let’s get started!