Techniques and approaches for monitoring large language models on AWS

This article discusses techniques and approaches for monitoring the performance and behavior of Large Language Models (LLMs) on AWS. As LLMs continue to grow in size and complexity, monitoring has become crucial for ensuring their safety and effectiveness.

Specifically, the article covers:

• An overview of a modular, serverless architecture using AWS services like AWS Lambda, Amazon CloudWatch, Amazon S3, and Amazon Kinesis for monitoring LLMs at scale
• Monitoring metrics like semantic similarity between prompts and completions, sentiment and toxicity analysis, and ratio of refusals
• Details on implementing modules to compute each of these metrics
• Using CloudWatch metrics and alarms to track and notify on unexpected metric values
• The importance of LLM observability for ensuring reliable and trustworthy use of LLMs