How to Evaluate Models with AWS Bedrock: A Comprehensive Guide

When working with Generative AI (Gen AI), it’s essential to thoroughly evaluate how different models perform in various conditions to choose the best one for your organization. AWS Bedrock offers powerful tools that allow you to carry out customized and detailed model evaluations, helping you compare and analyze the performance of different models. In this guide, we’ll walk you through the evaluation processes and modes available in AWS Bedrock, equipping you with the knowledge you need to make well-informed decisions.

Why Model Evaluations Matter

Evaluating AI models is key to understanding their strengths and weaknesses. By running these evaluations, you can pinpoint areas where a model may be lacking, ensuring it aligns with your organization’s needs and use cases. This process is also critical for assessing how well the foundation model (FM) integrates with your data, ensuring it remains unbiased and meets your goals.

Different Modes for Model Evaluation in AWS Bedrock

AWS Bedrock offers three distinct modes for running model evaluations, each catering to different needs and preferences:

1. Automatic Evaluation
2. Human Evaluation: Bring Your Own Team
3. Human Evaluation: AWS Managed Team

Each mode offers a varying level of human involvement and customization. Let’s dive into the details of each mode.

1. Automatic Evaluation

Automatic evaluations are powered by AWS’s infrastructure and are designed for efficiency. This mode evaluates the performance of a model using predefined or custom datasets without much manual intervention. Here’s how you can set it up:

Step-by-Step Process:

• Choose a Foundation Model: Select the model you want to evaluate and configure the inference settings, such as randomness, length, repetition, and diversity.
• Select a Task Type: Choose the task type for the evaluation, such as:
  • Text generation: Used for NLP-based content creation.
  • Text summarization: Summarizes text based on prompts.
  • Question answering: Provides answers based on text input.
  • Text classification: Classifies text into predefined categories.
• Define Metrics to Track: AWS Bedrock allows you to measure several key metrics:
  • Toxicity: Checks if the model generates harmful, offensive, or inappropriate content.
  • Accuracy: Assesses the model’s ability to provide factually correct information.
  • Robustness: Evaluates how the model performs when there are slight variations in input while keeping the meaning intact.
• Choose a Dataset: Use one of the available predefined datasets or upload your own. Some common options include:
  • BOLD (Bias in Open-ended Language Generation): Evaluates fairness across various domains like gender, race, and profession.
  • RealToxicityPrompts: Used to measure the level of toxicity in the model’s output.
  • TREX: Tests the alignment of natural language with knowledge base triples.
  • WikiText2: A dataset used for general text generation tasks.
• Specify Storage and Permissions: Define where the evaluation results will be stored in Amazon S3, and ensure the necessary IAM roles are in place to manage access.
• Run Inference and Scoring: AWS Bedrock will perform the evaluation and generate a scorecard that can be accessed from your S3 bucket.

2. Human Evaluation: Bring Your Own Team

If you need more nuanced insights, the “Bring Your Own Team” option allows you to involve your own team in the evaluation process. This mode adds a layer of human judgment to the evaluation, helping to capture more subjective aspects of model performance.

Step-by-Step Process:

• Select Foundation Models: You can evaluate up to two models at once.
• Pick a Task Type: Similar to the automatic evaluation mode, but you also have the option to create custom tasks tailored to your needs.
• Define Evaluation Metrics: Specify which metrics your team will use to assess the model’s responses. This can include options like:
  • Thumbs Up/Down: Simple binary approval or rejection.
  • Likert Scale: A 5-point scale for more nuanced ratings.
  • Ordinal Rank: A ranking system for comparing responses.
  • Likert Scale Comparison: A comparison of different responses using a 5-point scale.
• Set Up Your Work Team: Use Amazon SageMaker GroundTruth to manage tasks and user access.
• Provide Instructions: Guide your team on how to evaluate the model’s responses effectively.
• Submit the Job: After configuring everything, submit the job for evaluation.
• Review Results: Once the team completes the tasks, you can access the results from your S3 bucket.

3. Human Evaluation: AWS Managed Team

If you prefer a more hands-off approach, AWS also offers a managed workforce to perform the evaluations on your behalf. This mode allows AWS to handle all aspects of the evaluation, from workforce management to task execution.

How it Works:

• Provide Job Details: Give your evaluation a clear name to track it.
• Consult with AWS: Set up a meeting with AWS to discuss your evaluation needs, including task types, datasets, metrics, and storage locations.
• AWS Manages the Process: AWS will manage the workforce, ensuring that professionals with the right expertise carry out the evaluations.

Final Thoughts

Conducting thorough model evaluations is crucial for selecting the best AI models for your organization. Whether you choose the more automated approach or involve human judgment for deeper insights, AWS Bedrock offers a wide range of tools to support your evaluation process. By following the steps in this guide, you can ensure that the model you choose will align with your organization’s objectives and data requirements, setting the stage for successful Gen AI implementations.