Amazon announces Amazon Neptune ML: easy, fast, and accurate predictions for graphs
Today, Amazon announced Amazon Neptune ML, a new capability of Amazon Neptune that uses Graph Neural Networks (GNNs), a machine learning (ML) technique purpose-built for graphs, to make easy, fast, and accurate predictions using graph data. With GNNs, you can improve the accuracy of most predictions for graphs by over 50% when compared to making predictions using non-graph methods based on published research from Stanford University.
Today, Amazon announced Amazon Neptune ML, a new capability of Amazon Neptune that uses Graph Neural Networks (GNNs), a machine learning (ML) technique purpose-built for graphs, to make easy, fast, and accurate predictions using graph data. With GNNs, you can improve the accuracy of most predictions for graphs by over 50% when compared to making predictions using non-graph methods based on published research from Stanford University.
Making accurate predictions on graphs with billions of relationships can be difficult and time consuming. Existing ML approaches such as XGBoost can’t operate effectively on graphs because they are designed for tabular data. As a result, using these methods on graphs can take time, require specialized skills and produce sub-optimal predictions.
Using the Deep Graph Library (DGL), an open-source library to which AWS contributes that makes it easy to apply deep learning to graph data, Neptune ML automates the heavy lifting of selecting and training the best ML model for graph data, and lets users run ML on their graph directly using Neptune APIs and queries. As a result, you can now create, train, and apply ML on Neptune data in hours instead of weeks without the need to learn new tools and ML technologies.
You only pay for the AWS resources used such as Amazon SageMaker, Amazon Neptune, Amazon CloudWatch, and Amazon S3. Neptune ML is available to customers using Neptune engine version 1.0.4.1 (or later) and in all commercial regions Neptune is available in. To learn more about this feature, see the Neptune ML documentation page.