[D] Are traditional ML/ deep learning techniques used anymore in NLP, in production-grade systems?

A lot of companies are switching from the ML pipelines they've developed over the course of a couple of years to ChatGPT based/ similar solutions. Of course, for text generation use-cases, this makes the most sense.

However, a lot of practical NLP problems can be formulated as classification/ tagging problems. The Pre-ChatGPT systems used to be pretty involved with a lot of moving components (keyword extraction, super long regex, finding nearest vectors in embedding space, etc.).

So, what's actually happening? …

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