Automating the Hard Part of Data Science

The dirty secret of Natural Language Processing (NLP) is that data science teams must still build artisanal, one-off data connectors and preprocessing pipelines manually, which takes extensive time and significant effort. This is a serious bottleneck which inhibits time to value, and ultimately blunts the potential impact of expensive Ai projects.

What if that process could be automated, such that vast amounts of unstructured data could be quickly ingested, dynamically cleansed, then loaded into a Large Language Model? Like ETL for LLM? That's what the folks at Unstructured.io have built. Learn more by checking out this episode of InsideAnalysis! Host @eric_kavanagh will interview CEO Brian Raymond about connecting the worlds of structured and unstructured data.