An Alarm That Hears the Toast but Not the Gas
Let me start with a picture.
A smoke alarm rings every time someone burns a slice of toast. It is loud, it is annoying, and nothing is actually wrong. Then one day a gas tap is left on. There is no smoke, so the alarm stays silent, and that is the day it mattered.
The alarm is not broken. It measures smoke, and smoke is not the same thing as danger.
Teams that run machine learning models have the same kind of alarm. It is called a drift monitor. It watches the new data coming in, and rings when that data starts to look different from the data the model learned from. The hope is that it rings when the model starts to go wrong.
This lesson measures whether it does. I built five kinds of new data and sent each to a real model 100 times. Every time, I checked two things: did the alarm ring, and was the model actually worse?
The Words You Need First
Please read this slide slowly if any word is new. Every slide after it uses these words.
Row, column and batch. Data comes as a table. Each row is one example, here one district of California. Each column is one fact about it, like its location. A batch is a group of new rows checked together, here 500 at a time.
Median. The middle value: half the values are above it, half below.
Model and error. The model here guesses the typical house price in a district of California from 8 facts about it, like the median income and the location. Its error is how far each guess lands from the real price, averaged over many districts. Lower is better.
Label. The true answer for a row, here the real price. In real life labels often arrive late, weeks after the guess. That is why teams watch the inputs instead.
Training rows. The examples the model learned from. New rows are districts it never saw, standing in for the data it meets after launch.
Drift. New rows that look different from the training rows: richer districts, older houses, a different part of the state.
Drift monitor. A program that compares new rows with the training rows, one column at a time, and gives a number for how different they are. Above a chosen line, it rings an alarm. Two common ones:
- PSI (population stability index) cuts a column into 10 ranges and checks how the share of rows in each range has moved. Above 0.25 is the usual line for a big shift.
- KS (the Kolmogorov-Smirnov test) asks how likely a difference this big would be if nothing had changed. A very small likelihood rings the alarm, because a big difference is then unlikely to be luck. Here the alarm rings when the likelihood is under 5% in total. With 8 columns checked, each column gets an eighth of that, so a column rings below about 0.6%.
Prediction drift. Drift in the model's own guesses rather than in its inputs: the model suddenly guessing differently from how it guessed on the training rows. PSI can measure it too.
Harm. The model really doing worse. Here: its error on a batch of new rows is more than 10% above its usual error.