On this week's livestream, 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.
Build cross-cloud agents with the borderless Lakehouse
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Connecting structured and unstructured data is a practical challenge for AI agents. It would be interesting to see how teams keep the data accurate and traceable as agents work across different platforms.
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Joining structured and unstructured data with native AI functions in one workflow saves a lot of the usual pipeline headaches. Looking forward to the livestream.