AI Evals at a Glance: Heatmaps for Stakeholders
Visualizing AI evals with Inspect Viz Welcome back to our blog series on running, analyzing and visualizing AI evals. Last time we discussed how to design and run evals for analysis and visualization using inspect eval and inspect view . Many of these methods, such as rearranging the dashboard columns and sorting on them to determine model vs model and skill vs no-skill differences in metrics,…
Welcome to our blog series on running, analyzing and visualizing AI evaluations. In this installment, we'll explore how to leverage heatmaps to simplify complex data and make it easily digestible for stakeholders.
Heatmaps are a powerful tool that utilize color intensity to represent numerical magnitude, allowing us to quickly visualize performance trends across different variables. By using human color perception to distinguish gradients, heatmaps provide a clear and intuitive view of data patterns.
Imagine a high-stakes meeting where a data science lead needs to present performance breakdowns of multiple AI models across various tools. Traditional methods, such as reordering and filtering evals, can be challenging to read and present. Heatmaps offer a solution by automating the rendering of comparisons between different configurations, revealing clear patterns at a single glance.
Some key advantages of heatmaps include:
- Instantly revealing performance spectrums
- Leveraging the eye's natural ability to distinguish color gradients
- Highlighting top-performing configurations
- Demonstrating how changes to the model affect evaluation
- Quickly identifying skill gains and model ceilings
However, it's important to note that heatmaps have limitations. They only map two axes to a single color intensity, and cannot simultaneously display additional metrics like token costs or system latency. Additionally, collapsing samples for a given configuration removes information needed to determine result consistency.
To fully utilize heatmaps, we need a pipeline that converts raw telemetry metrics into spreadsheets and Business Intelligence (BI) dashboards. In the next part of this series, we'll build a Python script to process the output data for analysis in Google Sheets and Data Studio.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
