As an MLE who comes from backend web dev, I have flip-flopped on notebooks. I initially felt that everything should be in a python script. But I see the utility in notebooks now.
For notebooks in an ML pipeline, I find that data issues are usually where things fail. Being able to run code "up to" a certain cell and create plots is invaluable. Creating reports by creating a data frame and displaying it as a cell is also super-handy.
You say, "dial some logic in", which is begging the wrong question (in my experience, at least). The logic in ML is usually very strait forward. It's about the data coming into your process and how your models are interacting with it.
Underrated comment. At my place of work, I find this to be a huge part of the MLE job. Everyone knows R but none of the cloud tools have great R support.
How would that work in a public school setting? Seems like there are too many kids and not enough teachers/time to implement this across all subjects.
If you are right, and oral exams become the best way to evaluate student learning, then I could see smaller private schools becoming more popular as a correlation.
Its counter intuitive at first glance, but in my area (west coast) it is actually the renter-friendly voters who scare off development. Too many rental protections lead to developers opting for development in red-er pastures.
Imagine you invested $100 into my company. Let's say my company specialized in doing absolutely nothing, as in, the company had a bank account and produced/sold nothing. In our little toy world where you always make money on investments, two weeks in I returned $150 to you (you've made $50).
This brings up questions like "where did that extra $50 come from", but maybe you get the point of the original comment now.