From Unstructured to Structured Data with LLMs

Learn how to use large language models to extract insights from documents for analytics and ML

From customer emails to PDFs, every organization has mountains of unstructured text, and buried within are insights that can inform decision-making.  

Many teams have started using LLM-powered search and Q&A systems to retrieve insights from their unstructured data. While these systems are good for ad-hoc Q&A, they are not optimized for large scale production-grade analytics use cases. 

Better results can be obtained by using an LLM to convert documents into tables via large batch jobs for downstream analytics and ML use cases—directly on top of a warehouse like Snowflake. Join this webinar to learn how to easily generate insights from unstructured data by customizing an open-source LLM with Predibase and Ludwig, the open-source declarative ML framework.

In this on-demand webinar and demo, we’ll show you how to leverage state-of-the-art technology to:
  • Define a schema of data to extract from a large corpus of PDFs
  • Customize and use open-source LLMs to construct new tables with source citations 
  • Visualize and run predictive analytics on your extracted data  

Watch the on-demand webinar

Arnav Garg
Machine Learning Engineer
Wael Abid
Machine Learning Engineer
Jeffery Kinniso
Machine Learning Engineer