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AWS-native multi-tenant RAG
AI that works in production starts with the right architecture. In this webinar, we’ll walk through RAG fundamentals, AWS services, vector stores, and multi-tenant patterns with guidance on how to match them for scale, performance, and cost.
Agenda
- Introduction to RAG
What Retrieval-Augmented Generation is and why it matters - The AWS stack for RAG
Overview of AWS services (Amazon Bedrock, Amazon Knowledge Bases, AWS Lambda, etc.) - Vector stores on AWS
Comparison of available options and their benefits - Multi-tenant patterns
Overview of Pool, Bridge, Silo patterns for RAG systems - Pattern-Vector store matching
Best practices - Conclusions and Q&A
Key topics
- what RAG is and how it defines the way we design AI applications;
- AWS services for building RAG solutions, including Amazon Bedrock, Amazon Knowledge Bases, and serverless compute;
- how Amazon OpenSearch Serverless, Amazon Aurora, and Amazon S3 Vectors compare as vector stores options;
- multitenancy patterns for RAG systems: Pool, Bridge, and Silo approaches and their trade-offs;
- how to match the right vector store with your multitenancy requirements, performance, and cost goals.
Speaker
Amazon Bedrock
Amazon Bedrock
The platform for secure and private Generative AI servicesEnter the world of generative AI with Amazon Bedrock — a fully managed service offering top of the line foundation models, including Amazon's own AI model lineup.
Thanks to its serverless design, Amazon Bedrock eliminates the complexity of running AI at scale, and lets you seamlessly integrate Generative AI into your applications using familiar AWS services, such as AWS Lambda or AWS Fargate — whether it's your first steps with AI, or you are already revolutionizing the market.
Generative AI services