With the growing variety of foundation models (FMs) available, finding the right one for your specific use case is crucial. Amazon Bedrock makes this easier by providing powerful tools to select, evaluate, and compare the best-performing models for tasks like text generation, classification, summarization, and more.
Here's how you can leverage Amazon Bedrock's Model Evaluation feature to make informed decisions:
1️⃣ Automatic vs. Human Evaluation
Bedrock offers automatic evaluation using predefined metrics like accuracy, robustness, and toxicity, allowing you to assess model performance quickly. Suppose your use case involves subjective criteria such as relevance, style, or alignment with brand voice. In that case, you can opt for human evaluation workflows, which allow human reviewers to assess model responses based on custom metrics.
2️⃣ Experimentation Made Simple
You can bring your dataset or use built-in datasets to run evaluations across multiple models. Amazon Bedrock enables you to conduct side-by-side comparisons between models, helping you identify the one that best fits your text generation or question-answering needs. You can optimize the balance between performance and cost by iterating between different models and evaluation criteria.
Some limitations I found when testing the Model Evaluation feature
1️⃣ Limited Model Coverage: Model evaluation only supports specific types of models(only Amazon, Meta, and Mistral AI), primarily text-based large language models (LLMs). This limits its use if your applications require other types of models, such as multimodal or image-based models
2️⃣ Predefined Evaluation Metrics: While Amazon Bedrock supports several built-in metrics (e.g., accuracy, robustness, toxicity), these may not be sufficient for highly specialized or domain-specific use cases. Custom metrics can be set up via human evaluations, but this requires additional time and effort to define and implement
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