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Abstract
IBM Db2 now supports native vector storage and similarity search, with built-in connectors for popular AI frameworks like LangChain and LlamaIndex. And in the next major release, you will be able to call an AI model directly from a SQL query. But what does that mean for data professionals, how does it actually work, and why should you care?
In this session, we walk through Db2's AI features for building AI applications. We cover how Db2 finds information by meaning using vector search, how it connects with the AI frameworks your developers already use, and what calling an AI model from SQL will look like in practice.
We bring it all together with live demos so you can see these features in action, not just on slides.
This session is ideal for DBAs and developers who are new to these capabilities. No prior AI experience required.
Speaker(s):
Shaikh Quader
Shaikh Quader is an AI Architect and Master Inventor at IBM, where he leads the engineering of AI capabilities in Db2. His work focuses on embedding intelligence directly into the database layer through vector search, in-database machine learning, and LLM integration via SQL, so that enterprises can run AI where their data already lives. He holds 14 patents and has 14 peer-reviewed publications in AI and data systems. He is completing a Ph.D. at York University on AI in relational databases. Shaikh writes about the practice of leading AI product engineering in his newsletter, AI Architect's Playbook, and shares his work on LinkedIn.
Marhababanu Chariwala
Marhababanu holds an HBSc in Computer Science from University of Toronto. She is a Software Developer in Db2’s Runtime team. She has over 5 years of experience working with multiple intra runtime disciples such as Rowstore, Columnar Data Engine, Relational Data Service and Access Plan Manager.
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