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Jay Yang

Executive Director, RiskLab -
UBS

Jay Yang is an Executive Director and Distinguished Engineer working for UBS. He leads the UBS RiskLab Model Service team which manages the largest data science lab within UBS serving more than 1,200 data scientists and ML/AI developers.

UBS RiskLab builds a suite of ML/AI and Data Analytic common platforms and solutions leveraging Azure native services to enable self-service capabilities for users to expedite Model/AI development and streamline production deployment. The RiskLab AI Common Ecosystem (AICE) platform, an integral platform of the UBS AI Playbook, provides a governed and tenanted RAG environment, streamlined AI Agent standardization, registration and deployment solutions, and end-to-end ML model continuous integration, training, deployment and monitoring processes. In addition, Risklab data science platform offers governed corporate data mesh access and containerized big data compute environments.

Jay has over 20 years experiencing working for technology startups, fintech and financial institutions. He has a passion for building reuseable IT systems and ML/AI platforms.

Session Presenting

Tuesday, November 19
5:30 PM - 6:15 PM Greenwich Mean Time
BRK190
The success of GenAI apps is decided by the accuracy of their responses. Using Retrieval Augmented Generation (RAG), you can improve accuracy by grounding GenAI app responses in your data. In this session, explore advanced RAG techniques in Azure Database for PostgreSQL including new vector search algorithms, parameter tuning, hybrid search, semantic ranking, and the GraphRAG approach. See how customers are using these techniques to deploy corporate development platform for GenAI apps.