Comprehensive and Accessible Model Evaluation for Foundation Models on Amazon Bedrock

As companies look to incorporate powerful AI capabilities like large language models into their applications, selecting the right foundation model is crucial. As generative AI models become increasingly sophisticated and widely adopted, it is crucial to have a comprehensive and accessible approach to evaluating their performance. Different models can have vastly different performance characteristics depending on the use case.

In our previous article we explored how you can use Amazon SageMaker Clarify and fmeval to evaluate foundation models in SageMaker Jumpstart or external foundation models respectively.

In this article, we will explore Amazon Bedrock's model evaluation capability that allows you to rigorously test and compare foundation models to identify the best fit for your specific needs.

To get started, you can go to Model evaluation in Amazon Bedrock console,

How it works ?: You can choose to create either an automatic model evaluation job or a model evaluation job that uses a human workforce.

Automatic model evaluation jobs:
Automatic model evaluation jobs allow you to quickly evaluate a model's ability to perform a task. You can either provide your own custom prompt dataset that you've tailored to a specific use case, or you can use an available built-in dataset.

Model evaluation jobs that use human workers: Model evaluation jobs that use human workers allow you to bring human input to the model evaluation process. They can be employees of your company or a group of subject-matter experts from your industry.

You will then select the model you want to evaluate.

Task type: Model evaluation jobs support common use cases for large language models (LLMs) such as text generation, text classification, question answering, and text summarization.

Once you select the task type, you will select the metrics and dataset,

Then, you will specify the S3 location where the results of the model evaluation job are stored. Then, choose or create an IAM service role that grants Amazon Bedrock permission to the S3 buckets specified in your model evaluation job and the models you selected.

You will then create the evaluation job.

Once the model evaluation is completed, you can check the evaluation summary.

This blog will help you get started with evaluating FMs with Amazon Bedrock Model Evaluation.

Note: Amazon Bedrock Model Evaluation now supports evaluating custom model import models

Foundation models are a key enabler to unlock generative AI's potential, but harnessing that power requires upfront work to identify the correct model for your requirements. Amazon Bedrock's new evaluation capabilities streamline this critical step - allowing you to rigorously assess, compare, and select the highest performing foundation model so you can build secure, reliable generative AI applications.

Resources:

• Choose the best performing model using Amazon Bedrock evaluations
• Amazon Bedrock model evaluation is now generally available
• Amazon Bedrock Model Evaluation now supports evaluating custom model import models