Jon’s interviews are always well-structured. This episode walked through the full ML lifecycle — from feature engineering through model monitoring — using real team setups rather than toy demos.
Most useful: the segment on model decay detection. The guest’s rule of thumb: monitor input data distributions first (cheap), model output distributions second (medium), and ground truth labels last (expensive but definitive). Obvious in hindsight, but I’d been doing it backwards.