LLMOps at Intermountain Health: A Case Study on AI Inventory Agents
Overview
Experience | In Person |
---|---|
Type | Breakout |
Track | Artificial Intelligence |
Industry | Health and Life Sciences |
Technologies | MLFlow, Databricks Workflows, Unity Catalog |
Skill Level | Intermediate |
Duration | 40 min |
In this session, we will delve into the creation of an infrastructure, CI/CD processes and monitoring systems that facilitate the responsible and efficient deployment of Large Language Models (LLMs) at Intermountain Healthcare. Using the "AI Inventory Agents" project as a case study, we will showcase how an LLM Agent can assist in effort and impact estimates, as well as provide insights into various AI products, both custom-built and third-party hosted. This includes their responsible AI certification status, development status and monitoring status (lights on, performance, drift, etc.). Attendees will learn how to build and customize their own LLMOps infrastructure to ensure seamless deployment and monitoring of LLMs, adhering to responsible AI practices.
Session Speakers
Mark Nielsen
/Lead MLOps Engineer
Intermountain Healthcare