Easily Evaluate and Choose the Best Foundation Model with Amazon Bedrock's Model Evaluation Feature

When it comes to selecting a model, accuracy, robustness, and custom metrics are key decision factors. With Amazon Bedrock's model evaluation feature, you can easily compare and assess multiple foundation models to choose the one that best fits your needs.
Whether it's automatic evaluation—using built-in preset algorithms to measure model performance, or manual evaluation—customizing subjective metrics like friendliness, style, and brand alignment, Bedrock offers powerful support. With just a few steps, you can start your evaluation task and get precise results.
🚨 How to Start an Automated Evaluation Task? Before running a model evaluation, you'll need to create an S3 folder to store the results. It's simple:

1. Open the S3 console
2. Find your bucket (bedrock-cloudlab-xxxxxx)
3. Create a folder called result to store the evaluation data

Step1 - Click on Model Evaluation on the left, and in the dropdown under Create model evaluation on the right, select Automatic.

Step2 - On the evaluation task details page, enter the evaluation name and description (optional).

Step 3. Task type, taking General Text Generation as an example.

Step 4 - On the metrics and dataset page, select the evaluation metrics and datasets as needed.
Here, we will use Toxicity, Robustness, and Accuracy metrics, along with the built-in dataset as an example:

Step 5. On the evaluation results save path page:
a. Click Browse S3.

b. Select the S3 bucket and the result path.

c. On the IAM permissions page, select the existing role bedrock-exec-role-cloudlab.

Step 6. Click Create.
Step 7. View the results.
Once the evaluation job is complete, you can view the detailed evaluation results on the job details page.

Conclusion
With Amazon Bedrock’s powerful model evaluation feature, you can easily assess and compare different foundation models to find the best fit for your needs. By following a few straightforward steps—such as selecting evaluation metrics, choosing datasets, and configuring IAM permissions—you can efficiently set up and run automated evaluation tasks. Once the task is complete, simply browse the results on the job details page to gain valuable insights into your model's performance. This streamlined process ensures that you can make data-driven decisions and optimize your model selection quickly and effectively.