Title

AWS re:Invent 2023 - Accelerate foundation model evaluation with Amazon SageMaker Clarify (AIM367)

Summary

• Mike Diamond, a principal product manager with SageMaker Clarify, introduces the session on foundation model evaluations with SageMaker Clarify.
• Large language models (LLMs) can produce errors such as hallucinations (plausible but inaccurate answers) and stereotyping, which can lead to significant consequences like loss of stock value or perpetuation of biases.
• SageMaker Clarify aims to mitigate risks associated with LLMs by providing tools for model selection and customization workflow evaluations.
• The session covers the importance of evaluating LLMs for quality and responsibility, especially in light of upcoming regulations and the need for consumer trust.
• SageMaker Clarify offers a preview of foundational model evaluations, integrating responsible AI into the ML workflow and allowing evaluations of any LLM.
• The tool provides a UI in SageMaker Studio, a Python SDK, and an open-source FM evaluation library on GitHub.
• Emily Weber, leading the Generative AI Foundation's technical field community, discusses LLM evaluation use cases and the process of using SageMaker Clarify for model evaluation.
• Taryn Heilman, a senior data scientist at Indeed, shares insights on how Indeed uses LLMs and the importance of evaluating them for fairness and responsibility.
• The session concludes with a demonstration of SageMaker Clarify's capabilities and a Q&A segment.

Insights

• The session highlights the growing importance of responsible AI, especially as LLMs become more prevalent in various industries.
• SageMaker Clarify's new features aim to simplify the evaluation process, making it accessible to users with varying levels of expertise.
• The integration of human evaluations alongside algorithmic metrics suggests a recognition of the limitations of purely automated assessments.
• The examples provided by Taryn Heilman from Indeed illustrate real-world applications of LLMs and the potential risks that need to be managed.
• The session emphasizes the need for continuous evaluation throughout the ML lifecycle, not just during initial model selection.
• The open-source availability of the FM evaluation library on GitHub indicates AWS's commitment to community collaboration and transparency in the development of responsible AI tools.
• The demonstration of SageMaker Clarify's UI and pipeline integration showcases the practical application of the tool in a user-friendly manner.