Building the Intelligent Data Layer for AI: Architecting Scalable GenAI Apps
|How to build intelligent data layers using scalable GenAI apps with AWS and YugabyteDB
Artificial Intelligence thrives on data, and a modern, resilient database architecture is the foundation of every successful AI application. As GenAI applications are inherently cloud-native, ensuring resilience, scalability, and real-time performance is critical to prevent downtime and deliver seamless user experiences.
In this webinar, explore how to design cloud-native database architectures using YugabyteDB that meet the evolving demands of AI and GenAI workloads. Learn how to build intelligent applications by combining AWS Bedrock’s managed foundation models, such as Amazon Titan and the latest Amazon Nova Pro LLMs, with YugabyteDB’s distributed SQL and advanced vector indexing (pgvector + HNSW) for high-throughput similarity search andRetrieval-Augmented Generation (RAG).
Experience a live demo showcasing how to implement a full-stack GenAI pipeline: extracting knowledge from documents, embedding it with Amazon Titan or Nova Pro models, and retrieving insights in real-time using YugabyteDB as the vector-enabled data layer. See firsthand how YugabyteDB integrates seamlessly with the AWS ecosystem to unlock scalability, continuous availability, and rich SQL capabilities for your intelligent applications.
Whether you're modernizing your data layer or building GenAI applications from scratch, this session will arm you with best practices, real-world strategies, and future-proof architecture patterns. Yugabyte and AWS maintain a strong collaboration, working together to deliver robust cloud-native solutions for GenAI applications.
Key Takeaways:
Database Architecture for GenAI on AWS: Learn how YugabyteDB, deployed on AWS, enables scalable, resilient, cloud-native systems for AI/ML workloads with transactional consistency and global availability.
Real-World RAG Pipeline Demo: Watch a live demo showing how to build a production-grade Retrieval-Augmented Generation pipeline using YugabyteDB, Amazon Titan or Nova Pro via Amazon Bedrock, and LangChain framework.
LLM Integration with AWS Bedrock: Understand how to use Amazon Bedrock to seamlessly invoke foundation models like Titan and Nova Pro, and how to pair these with YugabyteDB’s pgvector for scalable AI experiences.
Future-Proof Your AI Stack: See how YugabyteDB’s distributed SQL platform ensures elastic scaling, continuous uptime, and advanced SQL capabilities, making it an ideal choice for powering AI/GenAI applications on AWS.
