Model Evaluations: Prove Your Routing Policy Actually Works

In this guide, DigitalOcean introduces the Model Evaluations feature, available in Public Preview, which allows users to assess the effectiveness of various model inference strategies on the DigitalOcean platform, including imported models from Hugging Face. It addresses the common issue of routing policies failing under real-world conditions, emphasizing the importance of evaluating models on comparable metrics such as cost, latency, and output quality. The guide outlines a process for setting up and running evaluations across three strategies: using a single frontier model, deploying a task-specific fine-tuned model, and employing the Inference Router with optimized policies. It provides detailed steps for defining evaluation criteria, configuring datasets, setting up candidate models, and selecting evaluation judges and metrics. The goal is to determine the best performing approach before implementing changes in production, with a focus on achieving a balance between accuracy, cost, and latency. The guide underscores the importance of iterative testing and tuning of routing policies, encouraging users to rely on data-driven decisions rather than intuition when making production changes.