Machine Learning Tech Brief By HackerNoon
The Model Was Never the Bottleneck: What Shipping a Text Classifier Into a Government Office Taught
This story was originally published on HackerNoon at: https://hackernoon.com/the-model-was-never-the-bottleneck-what-shipping-a-text-classifier-into-a-government-office-taught . We shipped a Word2Vec+LSTM over a more accurate BERT, then load testing showed the model was 0.3% of the wall-clock time. The queue was five human reviewers. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning . You can also check exclusive content about #machine-learning , #nlp , #text-classification , #bert , #mlops , #human-in-the-loop-ai , #govtech , #model-selection , and more. This story was written by: @vladimirbesk . Learn more about this writer by checking @vladimirbesk's about page, and for more stories, please visit hackernoon.com . We built a classifier that sorts incoming citizen appeals into complaints, applications and proposals, and routes them to the right department. BERT was the most accurate model we tested. We shipped a smaller Word2Vec+LSTM instead. Then load testing showed that neither choice mattered much, because the queue was never in the GPU. It was in the five people doing review.
