IDUG Brazil 2026

The AI problems landing on the desks of DBAs and application developers this year don't all need the same model. Some call for a Large Language Model. Others need a forecasting model, a clustering algorithm, or an embedding model. Plenty can be solved by predictive techniques that have been working quietly for years. One drawer of the toolbox has been getting all the attention. The skill that matters most, knowing which tool fits the problem, has gone quiet. If your data lives in Db2, much of the toolbox is already there.

This session lays the toolbox out on the table. Two axes, what data you have and whether you have labels, organize the AI landscape into a 2×2 you can hold in your head: predictive and unsupervised models for structured data, the same for unstructured data, plus embeddings and generative AI.

Then we map every drawer to a Db2 capability you can use today. In-database machine learning for classification, regression, and clustering. Native vector storage and similarity search. Built-in integration with external embedding and language models. Python frameworks for retrieval-augmented applications and AI agents. We close with production patterns we've seen ship, across the full range of model types and use cases.

Whether you live in SQL or Python, you'll leave knowing which tool to reach for, and where to start.