Choosing the Best Foundation Models in Amazon Bedrock
Key Takeaways
| Aspect | Detail |
|---|---|
| Automatic Evaluation | Leverage predefined metrics like accuracy and robustness to expedite AI model evaluation. |
| Human Evaluation Workflows | Validate subjective metrics using simple human evaluation workflows setup. |
| Fast Iteration and Quality Assurance | Quickly iterate with models in the playground and ensure quality with human reviews before launch. |
Evaluating Foundation Models with Ease
Amazon Bedrock, now in preview, has ushered in a new era of simplicity for developers aiming to assess and choose the most fitting foundation models (FMs) for their AI projects. The service streamlines the process by providing options for both automatic and human evaluations, accommodating a wide spectrum of needs from hard data analysis to nuanced brand alignment.
- Automatic Evaluation Feature : Quick assessment using Amazon Bedrock’s automatic model evaluation.
- Efficiency: Saves time by eliminating the need for independent benchmarks.
- Custom or Predefined Data: Flexibility to use personal datasets or Amazon’s selected data.
Streamlined Automatic Model Evaluation
The bedrock of model evaluation now becomes less burdensome. With Amazon Bedrock’s automatic model assessment feature, one can use either their own dataset or Amazon’s carefully selected data accompanied by predefined metrics tailored to specific tasks. This eradicates the need to run your own benchmarks and allows for a more efficient model evaluation.
Custom-Tailored Human Evaluation Workflows
For those metrics that demand a human touch – think friendliness, style, or alignment to brand voice – Amazon Bedrock simplifies setting up human evaluation workflows. A few clicks and you can gather insights from either your team or an AWS managed team, complementing data with human experience and intuition.
- Human Touch in Evaluation: Incorporates human perspective for metrics like friendliness and brand alignment.
- Simple Workflow Setup: Easy setup for human evaluation processes.
- AWS Team Support: Option to use AWS managed teams for enhanced insights.
Navigating the Model Evaluation on Amazon Bedrock
Starting with a selection of relevant FMs and specific tasks, you can navigate through an easy setup dialog, choose your metrics, and specify a dataset. Once the evaluation job runs its course, a comprehensive report is generated, ready for review to inform your next move.
- User-Friendly Interface: Easy-to-navigate setup dialog for selecting FMs and metrics.
- Comprehensive Reporting: Detailed reports generated post-evaluation.
- Informed Decision-Making: Helps in making better decisions for AI project development.
Further reading