Freeze the Model, Move the Labels
Daniel built a home inventory classifier that sorts items into boxes. Then he bought three new boxes, and the model went stale. His instinct was to retrain weekly — but the real problem isn't the model, it's that his label set keeps changing. This episode digs into the two architectural answers: retrain on a moving label set, or freeze the encoder entirely and put the labels in a vector index instead. Along the way: why SetFit training costs about two and a half cents, why synthetic training data can beat real labels on one benchmark and fail silently in your garage, and why "incremental vs…
The skinny
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