Amazon Neptune Database now integrates with GraphStorm for scalable graph machine learning

Amazon Neptune Database with GraphStorm Integration

Today, we’re announcing the integration of Amazon Neptune Database with GraphStorm, a scalable, open-source graph machine learning (ML) library built for enterprise-scale applications. This integration combines Neptune’s OLTP graph capabilities with GraphStorm’s scalable inference engine, enabling customers to deploy graph ML in latency-sensitive, transactional environments.

With this integration, developers can train GNN models using GraphStorm and deploy them as real-time inference endpoints that directly query Neptune for subgraph neighborhoods on demand. Predictions—such as node classifications or link predictions—can then be returned in sub-second timeframes, closing the loop between transactional graph updates and ML-driven decisions. This unlocks use cases such as fraud detection and prevention, dynamic recommendations, and graph-based risk scoring.

Customers can also combine real-time inference results with graph analytics queries for deeper operational insights, enabling ML feedback loops directly within graph applications.

What to do

• Explore the integration in all regions where Amazon Neptune Database is available.
• Check out the announcement blog for a full walk-through: Modernize fraud prevention: GraphStorm v0.5 for real-time inference.

Source: AWS release notes