Academia does not have to run the model in production. It has few computational constraints, and most datasets do not have a feedback loop, requiring combating

Academia does not have to run the model in production. It has few computational constraints, and most datasets do not have a feedback loop, requiring combating drift, debugging, and retraining. Papers are often accepted when they equal or beat state-of-the-art. Not many academics have to deal with the business side of running models in prod. All of this leads to ease of implementation being overlooked. Especially on NLP, you see a lot of overengineering with deep neural nets (where the feature engineering is hidden inside the architecture). These models are hard to implement/reuse. But yeah: academia/theoretical machine learning creates the very tools for applied machine learning.