Explore the challenges of providing Large Language Models (LLMs) as a Service in this 12-minute lightning talk from the LLMs in Production Conference. Delve into key issues including scalability, model optimization, cost-effectiveness, and data privacy. Learn from Hemant Jain, a Machine Learning Inference expert at Cohere AI with experience developing NVIDIA's Triton Inference Server. Gain insights into the complexities of model footprint, fine-tuning, and the importance of balancing performance with resource management in the evolving landscape of LLM deployment and service delivery.
Challenges in Providing Large Language Models as a Service
Overview
Syllabus
Introduction
Model Footprint
Fine Tuning
Cost
Model Optimization
Data Privacy
Taught by
MLOps.community
