IDUG AU 2025

IBM Db2 offers a wide range of AI features, both built-in and external tools, for creating AI systems and solutions. These include over 200 built-in AI stored procedures and tools that work seamlessly with open-source AI ecosystems. In this workshop, participants will explore Db2’s AI support and use a selection of these features to implement practical AI models. The workshop consists of two parts. In the first module, participants will learn how to develop a supervised AI model using Db2’s built-in AI stored procedures. The entire model development and usage will be done using SQL within the database. In the second module, participants will learn how to create a Python model using popular Python packages outside of the database, and then deploy that model back into a Db2 database. By the end of the workshop, participants will have gained fundamental skills and hands-on experience in utilizing these AI capabilities. They will be able to apply these skills to their future AI projects. This workshop is designed to be beginner-friendly and does not require participants to have extensive knowledge in AI. Basic SQL skills and some Python programming skills are sufficient for attending and benefiting from this workshop. Notes: the maximum number of attendees at the workshop will be 30. Also, each attendee will need to bring their own laptop that has internet access and a browser. Also, before the workshop, they need to register for a free IBMid from this link: Create an IBMid.

This Hands-On workshop is designed to give you the knowledge you need to use RDS for Db2. The workshop will cover approximately 10 modules covering things like provisioning, backups, recovery and moving data to RDS for Db2. You will be provided a temporary AWS account to work through a series of exercises to reinforce the information that is presented during the workshop.

In this lab, you’ll experience how easy it is to create new datalake tables in Db2, including Iceberg and non-Iceberg tables, using open data formats like ORC and Parquet on object storage. Additionally, you’ll see how datalake tables created in Db2 can be shared directly with the watsonx.data open data lakehouse (and vice-versa) and how data and workloads can be offloaded from the enterprise data warehouse onto a lower-cost data platform. This means that workloads using this data can run wherever it makes the most sense – either through watsonx.data or through Db2’s own native query engine (or even other query engines that support these open formats). This flexibility allows you to optimize your AI and analytics workloads by selecting the right engine, for the right workload, at the right cost.