¿Por qué no los dos? Big skill issue, you can even route known problems to a trained in-house simple classifier and low-confidence problems to Jev-like "zero shot classifiers" or LLMs.
I feel like people in the field forgot everything.
Training in-house has never been a skill issue, it is a budget issue of collecting and labeling enough training data.
I'm sorry, can you restate - are you saying in house models are good enough even when there's no relevant training data?
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It will only get worse. Non-tech people will be (or are) very suspicious of anything trained in house. The bar to spend time/money on anything domain specific will only get higher.
They didn't forget, they saw that nobody was paying for anything else while the rest of the larpers showed up and literally do not know anything about ML and just think "lemme paste some chadbbd on this"
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