IDUG NA 2025

Have you ever tried to use an LLM? How exactly can they be used in a business environment? And how can Db2 help with the accuracy of what an LLM produces?
This lab will take you through the various components that are used to create a system that generates a RAG (Retrieval–Augmented Generation) for an LLM to use to answer a question. You will get a chance to experiment with a system that provides a environment where you can try out different prompts and settings to see how an LLM will respond to your question. 
You will also get to see how Db2 fits into this picture and what vector calculations (and Vector databases) do when generating a RAG prompt.
 
 

In today's data–driven world, the integration of robust database management systems with advanced machine learning platforms is crucial for deriving actionable insights and driving innovation. This presentation explores the synergy between IBM DB2 and AWS SageMaker, highlighting how this powerful combination can enhance data analytics and machine learning workflows.
IBM DB2, a highly scalable and reliable relational database management system, offers comprehensive data management capabilities. When integrated with AWS SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly, the potential for data–driven decision–making is significantly amplified.
This session will cover:
The architecture and key features of IBM DB2 and AWS SageMaker.
Best practices for integrating DB2 with SageMaker to streamline data preprocessing, model training, and deployment.
Real–world use cases demonstrating the benefits of this integration in various industries.
Attendees will gain a comprehensive understanding of how to leverage the strengths of both DB2 and SageMaker to enhance their data analytics and machine learning capabilities, ultimately driving more informed and strategic business decisions.