ML and generative AI

Make predictions on graph data without ML expertise

Neptune ML automatically creates, trains, and applies ML models on your graph data. It uses DGL to automatically choose and train the best ML model for your workload so that you can make ML-based predictions on graph data in hours instead of weeks.

Improve the accuracy of most predictions by over 50% *

Neptune ML uses GNNs, a state-of-the-art ML technique applied to graph data that can reason over billions of relationships in graphs so that you can make more accurate predictions.

*Neptune ML uses GNNs to make predictions that can be more than 50% more accurate than non-graph ML, based on published research from Stanford University.

Build context-aware graph apps with open-source LangChain Python framework

LangChain is an open-source Python framework designed to simplify the creation of applications using large language models (LLMs). Neptune integration with LangChain’s allows developers to use LangChain’s open-source framework to simplify the creation of context-aware applications.

Translate English questions into openCypher graph queries and return a human-readable response

With Neptune and LangChain, you can return a response based on the provided context and query a Neptune graph database using the openCypher query language. For example, you can use the Neptune openCypher QA Chain to translate English questions into openCypher queries and return a human-readable response. This chain can be used to answer questions such as “How many outgoing routes does the Austin airport have?”

For more details about the Neptune openCypher QA Chain, visit the open-source LangChain documentation.

Use cases

Fraud detection

Fraud detection

Companies lose millions (even billions) of dollars in fraud, and want to detect fraudulent users, accounts, devices, IP address or credit cards to minimize the loss. You can use a graph-based representation to capture the interaction of the entities (user, device or card) and detect aggregations such as when a user initiates multiple mini transactions or uses different accounts that are potentially fraudulent.

Identity resolution

Customer acquisition

An identity graph provides a single unified view of customers and prospects based on their interactions with a product or website across a set of devices and identifiers. Organizations use identity graphs for real-time personalization and advertising targeting for millions of users. Neptune ML automatically recommends next steps, or product discounts to certain customers based on characteristics like past search history across devices or where they are in the acquisition funnel.

Knowledge graph

Knowledge graph

Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. Neptune ML can infer missing links across data sources, identify similar entities to enable better knowledge discovery for all.

Product recommendation

Product recommendation

Traditional recommendations use analytics services manually to make product recommendations. Neptune ML can identify new relationships directly on graph data, and easily recommend the list of games a player would be interested to buy, other players to follow, or products to purchase.

How it works

Amazon Neptune How it Works Diagram


There are no up-front investments needed. You only pay for the AWS resources used such as Amazon SageMaker, Amazon Neptune, and Amazon S3.

Getting started

The easiest way to get started with Neptune ML is to use the pre-built AWS CloudFormation quick-start templates. You can also walk through the Neptune ML notebooks to see end-to-end examples of node classification, node regression, and link prediction using the pre-built CloudFormation stack.

Check out the product features

Learn more about Amazon Neptune features.

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