Stop Drowning in AI Models: A 3-Pillar Framework for Evaluation

Evaluating AI models shouldn't require weeks of setup or custom tooling for each comparison. This framework breaks model evaluation into three essentials: (1) fast integration through abstract classes that let you test any model in minutes, (2) quality datasets covering both typical cases and edge scenarios where models break, and (3) real-time dashboards that surface insights through both metrics and visualization. Built from real experience comparing multiple CV approaches simultaneously, this system helps engineering teams make confident decisions about which models deserve production resources—whether evaluating third-party solutions or choosing between internal experiments.