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For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.

Did you know that BigQuery can run GQL queries? BigQuery Graph easily uncovers connections in your datasets, alongside your relational SQL queries.

Did you know you can call a Gemini model directly from a SQL query in BigQuery? In this hands-on codelab, Ayo and Annie do exactly that, and use it to solve a real problem: converting messy, unstructured text into clean, structured data at scale. This is Episode 1 of our multi-part series where we build a fully functional, data-aware AI agent on Google Cloud. *What we cover:* Chapters: Speakers: Ayo Adedeji, Annie Wang Products Mentioned: Gemini, BigQuery.