Dave Bechberger

In this hands-on workshop build powerful generative AI applications by combining the capabilities of Amazon Neptune, generative AI, LLMs, and Model Context Protocol (MCP).

Retrieval Augmented Generation (RAG) applications use the power of generative AI to analyze private datasets. GraphRAG combines knowledge graphs with RAG to produce explainable responses that are grounded in the semantic relationships between concepts, entities, and the underlying content.

In this chalk talk, explore how to build a scalable serverless hybrid search application that combines vectors and graph data. Learn how this approach empowers you to leverage the benefits of vector-based search with the rich relationships defined in graphs to provide more comprehensive and explainable answers.

Retrieval Augmented Generation (RAG) applications use the power of generative AI to analyze private, differentiating datasets. However, baseline RAG can sometimes produce poor responses that lack explainability and contextual awareness and include conflated sources and spurious claims.

In this chalk talk, explore how to build a scalable serverless hybrid search application that combines vectors and graph data. Learn how this approach empowers you to leverage the benefits of vector-based search with the rich relationships defined in graphs to provide more comprehensive and explainable answers.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Purpose-built to store and navigate relationships, graph databases can be used to store fraud graphs, which contain information on how actors, transactions, and other identifiers are related to each other.

Knowledge graphs are a hot topic in the enterprise data space but they often suffer from a lack of rich connections within the data. Graph neural networks (GNNs) can help fill this gap by using the structure of connections within the data to predict new connections.

Machine learning techniques can help find hidden correlations in graphs with billions of relationships. These correlations can be useful when recommending products, predicting creditworthiness, identifying fraud, and more.

With the rise of graph databases, graphs are no longer just a data structure but a powerful set of capabilities at the persistence layer which data scientists can leverage to accelerate the speed to insight.