Title

AWS re:Invent 2023 - Evaluate and select the best FM for your use case in Amazon Bedrock (AIM373)

Summary

• Amazon Bedrock now includes a model evaluation feature in preview, which helps users select the best foundation model for their applications.
• The evaluation process considers the trade-off between quality, cost, and latency of model responses.
• Two main methods for model evaluation are automatic (using algorithms) and human (subjective assessment).
• The process of finding the right model is often long and tedious, involving finding a home for models, selecting metrics, obtaining datasets, setting up infrastructure, and combining automatic and human evaluations.
• Amazon Bedrock aims to simplify this process by providing curated datasets, automated and human evaluation metrics, and the ability to bring your own team or use an AWS managed team for human evaluations.
• Users can define their own metrics, especially for human evaluations, to ensure relevance to their specific business needs.
• The evaluation process includes selecting models, task types, metrics, datasets, and setting up a work team, with results presented in an easy-to-understand scorecard.
• Amazon Bedrock offers a variety of models from different providers, including AI21 Labs, Anthropic, Cohere, Meta, and Amazon's own Titan models.
• The evaluation feature is designed to reduce cycle time and provide a comprehensive suite of tools for evaluating models.
• The session included a live demo of setting up and running both automatic and human evaluations, as well as how to request an AWS managed work team.

Insights

• The addition of model evaluation to Amazon Bedrock addresses a significant pain point for customers who need to ensure that AI models align with their company's brand voice, data, and customer service expectations.
• The ability to bring your own datasets for evaluation is crucial for businesses to test models with data that is representative of their specific domain and customer interactions.
• The flexibility to define custom metrics for human evaluation allows businesses to assess model responses based on criteria that are unique to their operations, such as brand voice or writing style.
• The integration of model evaluation within Amazon Bedrock simplifies the workflow by providing a single platform for both development and production, reducing the need to switch between different environments.
• The live demo highlighted the practical steps involved in setting up evaluations, showcasing the user-friendly interface and the straightforward process of running evaluations on Amazon Bedrock.
• The session emphasized the importance of human evaluation in conjunction with automated methods, acknowledging that certain aspects of model performance, such as coherence and relevance, require human judgment.
• The presentation of results in an easy-to-understand scorecard format is designed to help users make informed decisions about model selection without getting bogged down in complex data analysis.
• The mention of future enhancements, such as the ability to select custom models, indicates ongoing development and improvement of the Amazon Bedrock platform.