Virtual Delta Tables: Building a Multimodal Lakehouse

Managing multimodal inference is one of the defining challenges facing AI teams. LLMs now consider documents, images, and videos, producing higher quality results and reducing time wasted with data prep. The lakehouse architecture solves part of the problem but many teams are struggling to incorporate an increasing variety of data into their workflow. Rather than ingesting, re-encoding, or modifying massive troves of existing data, multimodal teams are looking to work with original artifacts.This talk introduces Virtual Delta Tables and the associated open source code designed for multimodal inference. Virtual Delta Tables incorporate original data from different sources into a singular logical structure for ease of use by data scientists and ML engineers. By offering a Delta Lake interface the entire ecosystem from Databricks to DuckDB can be used in multimodal work. We'll highlight just how far you can push the lakehouse architecture in the rapidly changing AI data landscape!