Graph-based ML is a powerful tool that utilizes the connections between entities in the graph to provide more accurate predictions. Amazon Neptune ML uses graph neural networks (GNNs), powered by the Deep Graph Library (DGL), to make easy, fast, and more accurate ML predictions. Since launching in 2020, Neptune ML has been used for graph applications such as recommendation engines, knowledge graphs, entity/identity resolution, consumer 360, and fraud detection. In this session, learn how you can apply Neptune ML to solve for common use cases while reducing the time, complexity, and cost of maintaining systems through Neptune ML's automation of the model-building lifecycle.
Home » Vidyard Assets: All Players » Solve common business use cases with Amazon Neptune ML
Building a modern data architecture on AWS
Organizations are being challenged by the unprecedented scale of data as the amount of data under analysis ...
Most Recent Videos
Scale Like Amazon: Innovator's Mindset
Scale Like Amazon: Introduction to Working Backwards
Scale Like Amazon: Amazon's Culture of Innovation