Comment by reeboo

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.