Proactively monitor service performance with SLO alerts

The article argues that focusing on "burn rate" – the rate at which a machine learning model makes confident but incorrect predictions – is a more valuable metric than traditional error rate. This is because burn rate highlights areas where the model is most likely to fail in real-world scenarios, offering a clearer picture of its weaknesses and guiding more effective improvements. Essentially, knowing how a model is wrong (confidently, vs. uncertainly) is more actionable than simply that it's wrong.