Learn how to build cross-cloud agents and leverage BigQuery multimodality, AI functions, embeddings, and vector search with the borderless Lakehouse.
## SUMMARY
In this episode of Google Cloud Live, learn how to build cross-cloud agents using a borderless Lakehouse architecture. Explore how BigQuery handles multimodal data and native AI functions to generate automated descriptions, update product metadata, and generate embeddings.
Discover step-by-step techniques for joining structured and unstructured data, performing vector search, configuring data chunks, and connecting external platforms into your Google Cloud Lakehouse workflow.
## WHAT YOU’LL LEARN
* **Create ObjectRef external tables** for multimodal data analysis in BigQuery.
* **Use AI functions** to generate brand descriptions, image descriptions, and metadata.
* **Generate text embeddings** and perform vector search over multimodal datasets.
* **Structure data chunks** and manage single vs. multi-response column outputs.
* **Integrate cross-cloud components** with borderless Lakehouse tables.
#GoogleCloud #BigQuery #Lakehouse
Speakers: Brad Miro, Tilde Thurium
Products Mentioned:, Borderless Lakehouse, Big Query
