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Topic: "Structuring Healthcare Data for usage with LLMs"
Getting your data RAG-ready for usage within LLM’s is no easy task. When your data consists of various patient chart document types (e.g. PDFs, HTML, PPT not to mention handwritten notes) you will have your work cut out for you. Join us in a workshop where we will structure healthcare patient data for usage with LLM’s. With the help of ‘unstuctured.io’ packages we will transform, clean, chunk, generate summaries and embeddings, then write our ‘structured’ healthcare data to a vector database (weaviate) where it can be used to interact with LLMs or Chatbots.
Training outline:
- Why is Structuring Healthcare Data so important when creating a Healthcare LLM?
- Discuss RAG Architecture for a future Healthcare LLM and/or Chatbot.
- What does Unstructured.io do?
- Why do we use a Vector Database?
- Steps to structuring our data for usage!
- Storing our structured data in Weaviate.
- Querying our newly structured healthcare data.
Speaker#1: Audrey Reznik Guidera, AI Platform Specialist Solution Architect | Red Hat
Audrey is an AI Platform Specialist Solution Architect working for Red Hat. She focuses on helping customers with managed services, AI/ML workloads and next-generation platforms. She holds a degree in Computer Information Systems and has been working in the IT Industry for over 20 years from full stack development to data science roles. Audrey is passionate about AI and in particular the current opportunities with AIML at the Edge and Open Source technologies.
Speaker#2: Bob Kozdemba, Principal Specialist Solution Architect | Red Hat
Bob is a career pre-sales engineer with hands-on experience using best of breed open source technologies to solve AI/ML workflow challenges including MLOps, RAG and generative AI.
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