[braza]

A lot of great takes in this thread, but let me give more perspective being a MLE/MLOps Engineer myself around the math.

For me mathematics and statistics represent (a) unemployment insurance since it give me a lot of transitivity around roles in the space,(b) give me a good toolbox to talk as an equal with DS, and (c) for all implementation made by the Research Engineers/Data Scientists I can chime in and give insights and avoid waste of time and resources.

One example: When BERT was released (ca. 2018) I was working in a place where several Research Engineers and DSs wanted to use it in production for text classification.

The issue was that architecturally BERT was suboptimal due to a process called masking [1] that increases significantly the training time and the inference time was not so great. The alternative that I gave at that time was to use a mechanism called "Bag fo Tricks" [2] which its a very efficient modification of Bag of Words, but knowing math (and being on top of the literature) saved me from implementing something that would be inherently inefficient. Without having it it's hard to push back on DS/ResEng.

[1] - https://datascience.stackexchange.com/questions/97310/what-i... [2] - https://arxiv.org/abs/1607.01759