I partly agree with this but I think it's a bit overrated. There's no massive barrier to getting an actually good model into production. If the model seems promising but no one can figure out how to productionize it, it probably has fatal flaws as a model. There's no guarantee that a mess of Python code that somehow produces a nice AUC curve is actually doing anything valuable. As in science in general, there are many ways to fool yourself in data science.
I am not saying its a hard skill or anything. But asking your manager everytime to go find someone to put your notebooks in productions is not good strategy to succeed at work. If you can get your notebook pretty far into production and have software engineering skills it goes a long way.
I completely agree, I think we're saying the same thing. I guess I'm just emphasizing that it's hard to believe that such a person is even a good data scientist. Throwing everything over the wall is denying yourself a lot of valuable lessons about data science.
>I guess I'm just emphasizing that it's hard to believe that such a person is even a good data scientist.
Odd take to be honest. Like suggesting you cannot believe someone is a good materials scientist, because their CAD/mechanical design skills
are not up to par with a mechanical engineer.
Not sure how CAD skills are relevant for a materials scientist, but the ability to put models into production is very relevant to data science.
If you always throw over the wall, you probably don't understand how results that look good in a Jupyter notebook can easily fall apart in the real world.
