How do you handle coverage guarantees from split conformal prediction when the data shifts? [D]

Explain Like I'm Normal

Conformal prediction is a method for providing mathematical guarantees on model uncertainty, but it breaks when real-world data distribution shifts. Practitioners are debating how to maintain these safety bounds when production data no longer looks like the original training sets. The discussion focuses on whether to use automated recalibration or human-in-the-loop fallbacks when prediction sets become too large.