Model Evaluation in Amazon Bedrock to compare & choose the right FMs | Amazon Web Services
Unclear model performance can slow AI progress. This video on Amazon Bedrock's model evaluation feature shows how to compare foundation models using accuracy and robustness metrics, test with built-in or custom datasets, and review results stored in Amazon S3. Watch the video to get practical insight into smarter model selection.
What is Model Evaluation in Amazon Bedrock?
Model Evaluation in Amazon Bedrock is a capability that helps you systematically assess, compare, and select large language models (LLMs) and foundation models (FMs) for your generative AI applications.
Instead of manually testing multiple models in an ad hoc way, you can use Model Evaluation to:
- Run structured evaluations across different models
- Compare performance on your specific tasks and domains
- Consider different data modalities and other relevant factors
This makes it easier to narrow down which model is the best fit for your use case before you commit to building and scaling an application on top of it.
Why does choosing the right model matter?
Choosing the right model is a critical first step because LLMs and FMs can perform very differently depending on:
- The task (e.g., summarization, Q&A, content generation)
- The domain (e.g., finance, healthcare, customer support)
- The data modalities involved (e.g., text, possibly other formats)
- Other context-specific requirements such as tone, latency, or cost
If you pick a model without evaluating it against your real use case, you may end up with lower-quality outputs, higher costs, or more rework later. Model Evaluation in Amazon Bedrock helps you reduce that risk by giving you a structured way to compare models up front and align your choice with your business and technical needs.
How can I get started with Model Evaluation and related AWS resources?
To get started with Model Evaluation in Amazon Bedrock, you can:
1. Explore the Amazon Bedrock developer experience and documentation to understand how to configure and run evaluations for your use cases.
2. Watch the demo that shows how Model Evaluation simplifies comparing different LLMs and FMs so you can pick the right one for your application.
3. Use AWS learning resources and events to deepen your understanding of generative AI on AWS.
Additional AWS resources include:
- Developer experience overview for Amazon Bedrock: learn how to build and evaluate generative AI applications.
- AWS video libraries for product walkthroughs and event sessions.
- AWS re:Post, where you can ask technical AWS questions and get answers from a community of experts.
All of this sits on top of Amazon Web Services (AWS), a cloud platform that offers over 200 fully featured services from data centers around the world, used by millions of customers—from fast-growing startups to large enterprises and government agencies—to lower costs, increase agility, and reimagine how they build and run applications.
Model Evaluation in Amazon Bedrock to compare & choose the right FMs | Amazon Web Services
published by Derive Technologies
Derive Technologies, was founded in 2000 through the combination of two long-standing technology firms dating back as far as 1986; and incorporated as “Derive Technologies” in the beginning of 2001. Derive's team -- all of them already long-time collaborators at the time of the company's official founding -- continue to design and deliver progressive business-technology solutions that meet the challenges of New York Metro Area, national, and global enterprises, with a focus on on-going cost reduction. Starting as a local system integrator, Derive grew to become a value-added enterprise reseller (VAR), and, now, a recognized national and international IT business consultancy.
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