What are the biggest pain points in ML Engineering
Wondering is painful in the ML development cycle today? For example, is reproducibility a big need or are there parts of deployment a real hassle?
I'm not sure if things have changed significantly since I was doing ML (because it has been a while). But tuning hyperparameters was always a huge pain. So many magic numbers to deal with...