Check-in & Networking

Data to Decisions: Problem framing with data for business value

In this insightful keynote, data strategy expert Ben Skrainka addresses a crucial challenge: making sure data models deliver real business value. He explores evidence-based methods to validate whether models truly answer key business questions, assess data sufficiency, and establish model trustworthiness. Participants will learn practical approaches to meet business goals, ensuring that data-driven decisions create measurable impact in today's generative AI enterprise.

Foundations of scalable RAG for generative AI use cases

Unlock the foundation of enterprise-ready Retrieval-Augmented Generation (RAG) with PostgreSQL pgvector and Amazon Bedrock Knowledge Bases. Explore how developers efficiently build scalable and cost-effective AI applications including conversational AI, real-time semantic and hybrid search, and intelligent recommendation systems. Learn how to streamline development using Amazon Bedrock, LLMs, and a vector database to enhance retrieval accuracy and automation. We'll also explore agentic AI architectures, enabling seamless integration with enterprise data while optimizing performance and cost.

Rapid Prototyping: Build effective RAG pipelines for generative AI use cases

​​In this hands-on session discover how to quickly prototype RAG pipeline using Amazon Bedrock and Aurora PostgreSQL pgvector. Building a RAG pipeline involves data ingestion, chunking, embedding, and iterative tuning to optimize data quality. Amazon Bedrock simplifies this process with Knowledge Bases, automating unstructured data handling and providing fine-grained tuning options. Its built-in RAG evaluation features help assess and refine pipelines using custom datasets. We'll explore how to build, manage, and optimize RAG pipelines with Amazon Bedrock and Aurora PostgreSQL pgvector, followed by a live code walkthrough showcasing the end-to-end process in action.

Lunch & Networking

Partner Session - Building trustworthy RAG with Cleanlab.AI

​In this hands-on session, learn to build reliable RAG applications using innovative tools. We'll guide you through creating a foundational RAG system with Aurora PostgreSQL/pgvector and Cohere, then demonstrate how to integrate Cleanlab.AI to enhance application dependability. You'll discover identifying and resolving common RAG challenges, including knowledge gaps, retrieval inaccuracies, and AI hallucinations. This hands-on workshop is ideal for developers and AI engineers seeking to construct more robust AI applications. Gain practical insights into building trustworthy RAG systems, harnessing Cleanlab.AI, Aurora PostgreSQL, and Cohere.

Practical strategies for production launch with Generative AI Innovation Center

​​Using a real-world example of a RAG application, we'll highlight how we help customers to quickly develop prototypes and scale it to production. This session explores the journey from PoCs to enterprise-ready production solution, focusing on selecting optimal LLMs and vector databases for specific business objectives. Join us to learn practical strategies for moving beyond prototypes and building scalable, production-grade generative AI solutions for your use cases and business objectives.

Q&A and Networking