Yinxi Zhang

Learn the essential, Databricks AI FDE -tested playbook for deploying production Genie. Start by scoping topic-focused Spaces that leverage Unity Catalog and knowledge-store metadata along with parameterized example SQL rather than free-text queries. This approach drives both clarity and speed. Before scaling out, establish pragmatic, business-centric benchmarks using trusted assets and value dictionaries to measure what truly matters. Integrate Genie into multi-agent workflows through the Conversation API, Teams, and the integration into Databricks’ Agent Framework. Operationalize at scale with pre-joined and trimmed tables, audit-driven monitoring, and a lightweight review cadence. We'll share proven do's and don'ts, common pitfalls to avoid, and a reusable checklist to accelerate your time-to-value, all grounded in Unity Catalog metadata and real-world enterprise deployments.

There are many ways to implement entity resolution (ER) system — both using vendor software and open-source libraries that enable DIY Entity Resolution. However, generally we see common challenges with any approach — scalability, bound to a single model architecture, lack of metrics and explainability, and stagnant implementations that do not "learn" with experience. Recent experiments with transformer-based approaches, fast lookups with vector search and Databricks components such as Databricks Apps and Agent Eval provide the foundations for a composable ER system that can get better with time on your data. In this presentation, we include a demo of how to use these components to build a composable ER that has the best outcomes for your data.