Machine learning in most shops is in desperate need of better engineering practices. PhDs are brilliant but they tend to only apply that brilliance narrowly. They often don't really care about things like re-usability, performance, testing, monitoring, or even version control. There is large demand for 'model engineering' to basically help build pipelines to get them data and to create reusable frameworks so models can be iterated on and validated and deployed quickly and easily. It's easy to say the new model has a better AOC than the old one but the true test is whether the model in front of users actually does what it's supposed to do.
