When evaluating generative models on AWS Bedrock, developers should consider three main categories of metrics: output quality, cost efficiency, and user/business alignment. These factors ensure models are not just fast but also reliable, sustainable, and fit for purpose.
Output quality metrics are critical for assessing how well the model meets task requirements. For text generation, metrics like perplexity (how confidently the model generates output) and BLEU/ROUGE scores (for comparing output to reference texts) can quantify accuracy. However, task-specific measures matter too: for chatbots, you might track conversational coherence by analyzing if responses stay on-topic, or use human evaluators to rate output relevance. For image generation, metrics like Fréchet Inception Distance (FID) compare synthetic and real data distributions. For example, a marketing team using Bedrock’s Stable Diffusion models could use FID to ensure product images look realistic. Additionally, diversity metrics (e.g., unique n-grams in text or color variance in images) prevent repetitive or generic outputs, which is vital for creative applications.
Cost efficiency goes beyond raw compute speed. Track cost per request, which depends on input/output token counts for text models or resolution for image models. For instance, Claude-v2 on Bedrock charges per token, so optimizing prompts to reduce input length directly lowers costs. Compare costs across models: using Titan for basic summarization might be cheaper than a larger model like Jurassic-2. Also, consider operational costs—if a model requires frequent retries due to errors, this adds latency and expense. Tools like Bedrock’s Provisioned Throughput can help balance cost and performance for high-volume use cases. For example, a customer service chatbot handling 10,000 requests/day could save thousands monthly by selecting a cost-effective model tier.
User and business alignment metrics ensure the model adds real-world value. Measure task success rate (e.g., percentage of code snippets that compile correctly from a code-generation model) or user satisfaction scores via surveys. For moderation-focused models, track false positive/negative rates in flagging harmful content. Also, monitor latency variance—consistent 2-second responses are often better than fluctuating between 0.5s and 5s, even if average speed looks good. For example, a healthcare app using Bedrock’s models to generate patient summaries would prioritize accuracy (via manual review spot-checks) over raw speed to avoid critical errors. Finally, align metrics with business KPIs, like reduced support tickets after deploying a chatbot, to demonstrate ROI.