The link points to the original paper now (https://www.nber.org/papers/w32604) so this discussion isn't applicable going forward.
I thought the pressure of applying for jobs was too much in 2000 when objectively it was much easier, and just went to work in a plastic factory. After learning programming I've only applied to seven jobs in nine years, and it takes me weeks of procrastination for each one. Just like married people are all glad they don't have to deal with the dating apps today, I'd probably be unemployed again if I went back on the job market. I have endless appreciation for recruiters helping me through the friction parts, and wish there were fixers like that for everybody.
> Option 2 - Hand it off to DevOps. The other option is to have data science produce prototypes that can be on Notebooks and then have a devops team whose job is to refactor those into an application that runs in production. This process makes things less fragile, but it is slow and very expensive.
I've never understood why this is so hard. Every time data science gives me a notebook it feels like I have been handed a function that says `doFeature()` and should just have to put it behind an endpoint called /do_feature, but it always takes forever and I'm never even able to articulate why. It feels like I am clueless at reading code but just this one particular kind of code.