IDUG NA 2025

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.

Join me on a journey through our first year of deploying SQL Data Insights (SDI) into production. Discover how SDI impacted our business processes, changed stakeholder perceptions of the mainframe, and enhanced our approach to data analysis.
We will start with a review of our first use case and discuss the resources consumed for training and scoring, as well as the impact SDI had on our business processes. 
SDI's most evident benefit is its ability to analyze data where it sits without having to export, transfer and reload it, and with minimal data science dependency. But how does it compare to more traditional approaches? To answer that, we'll compare SDI with other machine learning algorithms by applying them to a frequently used dataset for predicting diamond prices. We'll delve into the accuracy of the algorithms and see how SDI stacks up against data scientists' most loved algorithms.
Following that, we'll explore a brand–new use case that applies the new commonality–function in Db2 FL504. We'll discuss its usefulness, resource requirements, and potential impact on business processes, providing more practical insights into the power of SDI.
And finally, I'll share a use case that demonstrates how to identify outliers using SDI with Db2 monitoring data, ready to be applied in your own shop. 
Don't miss this opportunity to gain practical insights into the power of SDI and how it can transform your data analysis!

Relational databases serve as the primary data source for enterprise AI projects. Traditionally, when an AI project requires data from a relational database, engineers duplicate the data onto a separate system for model development and inference. However, this duplication process presents various challenges, including network latency, complex data governance, and the need for multiple APIs. These challenges often impede the successful implementation of AI models in production for many enterprise projects. To address these obstacles, IBM Db2 offers in–database AI capabilities and external tools. With Db2, ML practitioners can create and deploy AI models directly within their databases, significantly simplifying the complexities of AI pipelines. By leveraging in–db AI features, enterprises can safeguard sensitive data, expedite inferencing processes, and seamlessly integrate AI models with downstream business applications. In this talk, I will present the AI capabilities and tools provided by Db2. I will showcase how these capabilities can be effectively utilized in upcoming AI projects, demonstrating their potential to transform enterprise AI implementations.

Join the product management to get an update on the new version of Db2 Analytics Accelerator V8 in detail. Learn about Accelerator-to-Accelerator Copy, New Performance Warehouse and more. Learn about new and latest member of IBM Z family, z17 & LinuxONE. How it makes a difference with Db2 Analytics Accelerator.

Generative AI presents a transformative opportunity to address the knowledge and skill gap in mainframe environments, particularly for Db2 for z/OS. By leveraging large language models (LLM) and Retrieval–Augmented Generation (RAG) patterns, organizations can create intelligent systems that make mainframe expertise more accessible and actionable. When integrated with automation frameworks, these solutions not only simplify complex workflows for early tenure DBAs —reducing the steep learning curve—but also enable experienced DBAs to focus on strategic, high–value tasks. This session explores how generative AI can be applied to enhance mainframe productivity, preserve institutional knowledge, and create a scalable foundation for skills development across diverse user groups.