Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

This article discusses how to evaluate large language models (LLMs) using Amazon SageMaker managed MLflow and FMEval, focusing on tracking and assessing model performance across various dimensions.

• FMEval is an open-source library for evaluating foundation models with three main components:
  • Data config
  • Model runner
  • Evaluation algorithm
• SageMaker MLflow provides a tracking server for managing machine learning experiments
• The approach enables systematic evaluation of LLMs for:
  • Accuracy
  • Toxicity
  • Factual knowledge
• Supports evaluations for models from Amazon Bedrock and SageMaker JumpStart

The method helps developers create a robust, scalable workflow for assessing LLM performance, tracking results, and making data-driven decisions in generative AI development.