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Session title: Vector Support in IBM Db2: Powering Modern AI Applications
Session abstract: Large language models (LLMs) have transformed AI, enabling a new class of applications known as language apps. Enterprises are increasingly adopting these apps for tasks such as chatbots, document summarization, question-answering, and information retrieval, driving renewed interest in AI.
A key component of language apps is the generation of vector embeddings using language models. This process converts unstructured content, such as PDFs, into AI-readable representations. These embeddings power vector similarity search, which delivers more precise semantic retrieval than traditional search methods. Vectors require efficient, cost-effective, and secure storage, along with optimized search techniques. To address these needs, Db2 has recently introduced its first phase of vector support, featuring native vector type, vector similarity search, and a range of vector operations. In this presentation, the speaker will explore Db2’s vector capabilities and how they can be leveraged to build modern AI applications with Db2.
Speaker bio: Shaikh Quader leads AI Engineering at IBM Db2, building AI products including vector search and in-database machine learning. He joined IBM twenty years ago as a software developer and moved into AI in 2016.
An IBM Master Inventor, Shaikh holds several patents in AI and data systems and has published fifteen peer-reviewed papers on machine learning systems and AI engineering. He is pursuing his Ph.D. in AI and relational databases while leading research partnerships between IBM and Canadian universities. He has mentored over 100 engineers in AI and career development.
Shaikh writes about AI systems, careers, and technical leadership through his newsletter “AI Architect’s Playbook” and on LinkedIn.
LinkedIn: https://www.linkedin.com/in/shaikhquader/
Newsletter: https://aiarchplaybook.substack.com