[kajecounterhack]

> Engineers - and especially designers - also struggle with edge cases when things go off the happy path. It's often easy to make an ML prototype that works in 90% of cases, and get a project started - but a nightmare to solve enough the edge cases for a production grade system. Finding and papering over and designing around all those edge cases effectively can require a deep bag of tricks a pure software engineer won't have.

YMMV. Finding and papering over the things that prevent a model from being deployable can also require a deep bag of engineering tricks that an average ML research scientist does not have. In my personal experience I've seen teams burned by this more often than the other way around.