[mtqwerty]

Readjusting expectations for pre-processing was one of the biggest differences I noticed going from NLP courses to working on NLP in production. For the amount of pre-processing learning material there is, I expected it to be much more important in practice.

I feel lucky to gotten into NLP when I did (learning in 2017/2018 and working in the beginning of 2020). Changing our system from glove to BERT was super exciting and a great way to learn about the drawbacks and benefits of each.

[PeterisP]

IMHO it's not a difference between courses and production, but rather about the difference between preprocessing needs of different NLP ML approaches.

For some of NLP methods all the extra preprocessing steps were absolutely crucial (and took most of the time in production) and for other NLP methods they are of limited benefit and even harmful - and it's just that in older courses (and many production environments still!) the former methods are used, so the preprocessing needs to be discussed, but if you're using a BERT-like system, then BERT (or something similar) and its subword tokenization effectively becomes your preprocessing stage.