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Learn to build AI-powered data lakehouses that can analyze themselves and predict failures before they occur in this conference talk from the MLOps World GenAI Summit 2025. Discover how to leverage large language models and Apache Iceberg metadata to create intelligent data infrastructure that goes beyond traditional chatbots to provide deep analytical capabilities. Explore practical techniques for automatically analyzing SQL dependencies, identifying performance bottlenecks and zombie processes, detecting storage overuse and customer PII exposure, and finding compaction opportunities within your data platform. Master the process of extracting and embedding metadata from Iceberg tables into AI-powered systems that can interpret and reason over complex data architectures. Understand why manual analysis fails at scale and how LLMs can solve metadata challenges that worsen as systems grow. Build a natural language interface that allows querying data infrastructure in plain English while uncovering hidden business value in schemas, dependencies, and usage patterns. Gain insights into how simple structural changes in tables can yield massive performance improvements and learn to create self-aware, intelligent, and explainable data lakehouses that reduce onboarding time from weeks to days.
