𝗪𝗵𝘆 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗦𝗰𝗼𝗿𝗲𝘀 𝗟𝘆𝗲
You trained your model. The metrics looked great. You deployed it.
Six months later, something is wrong. Your accuracy dashboard looks fine, but the model is failing.
This happens because of distribution shift. The data in production is different from your training data. This shift breaks calibration.
If you use Mixture-of-Experts (MoE) architecture, you face a specific risk.
Calibration means if a model says it is 80% confident, it is right 80% of the time. In MoE models with soft routing, this breaks silently.
Soft routing blends multiple experts to get a result. Even if every expert is calibrated, the combined score becomes unreliable when the input data changes. Different routing patterns appear that the model did not see during training.
Hard routing is more robust. It sends an input to only one expert. The confidence stays tied to that specific expert.
How to fix this:
- Use Adversarial Reweighting: Train your model on hard examples. Use an exponential tilt to emphasize high-loss examples during training.
- Use Robust Filtered Loss: Focus training on cases where the expert blend performs worse than a single expert.
What to do right now:
- Monitor Expected Calibration Error (ECE): Track if your confidence scores match your actual accuracy.
- Plot Reliability Diagrams: Watch for curves that bend away from the diagonal line.
- Track Input Drift: Use tests like Kolmogorov-Smirnov to see if your production data has changed.
- Use Temperature Scaling: This is a fast patch to fix confidence scores after deployment, though it is not a permanent fix.
Calibration is a system property. Calibrated parts do not always make a calibrated whole.
Have you faced calibration drift in production? Tell me your monitoring setup in the comments.
Optional learning community: https://t.me/GyaanSetuAi