Session 4 : Harnessing Local LLMs: A Practical Guide for Secure Data Science
Presentation Type
Oral
Student
No
Track
Tools
Abstract
The adoption of Large Language Models (LLMs) for data analysis often raises privacy and security concerns due to reliance on cloud services. This talk explores running LLMs locally to address these challenges, comparing performance across three different hardware configurations. Attendees will learn about the development of a custom data analysis tool incorporating local LLMs and see a live demonstration of its capabilities. The session emphasizes practical strategies for integrating open source LLMs locally, enabling secure and efficient analysis of sensitive data.
Start Date
2-7-2025 11:00 AM
End Date
2-7-2025 12:00 PM
Session 4 : Harnessing Local LLMs: A Practical Guide for Secure Data Science
Jacks' Place (Room 050)
The adoption of Large Language Models (LLMs) for data analysis often raises privacy and security concerns due to reliance on cloud services. This talk explores running LLMs locally to address these challenges, comparing performance across three different hardware configurations. Attendees will learn about the development of a custom data analysis tool incorporating local LLMs and see a live demonstration of its capabilities. The session emphasizes practical strategies for integrating open source LLMs locally, enabling secure and efficient analysis of sensitive data.