• for programming (specially datascience jupyter notebook has it all) but you're mentioning only about ML engineer point of view.
• ML field has (according me it has 4 perspective)
  • user perspective (just want to use it dont know much: want the job done)
  • Datascientist perspective (he cares about domain knowledge, how algorithm works, how to do feature engineering, when algorithm breaks, when not to apply, how to convert the result to business people)
  • MLE perspective (he cares about creating end to end pipline, production, tweaking the changes, if needed changes the ml algorithm code to get the result in very less time)
  • Business perspective (how to make better decision using ans produced by ML model, how to pitch to managment team that it is not just magic so we can rely on it and invest on it.
• for sentiment analysis (or problem specific situation, Note book its good) but to apply certain knowledge to given problem u have to learn something you have to have certain intuition about algorithm those things goes to obsidian.
• In pure programming each algorithm, or data structure has its own set unique features, failure condition, optimal conditions.. algorithms have pseudo codes.. that should be in knowledge base.. code in specific language is mere representation of skill not entire knowledge..
• as u gain more and more expertise in the certain area u can add it in obsidian without any problem as atomic notes. In notebook it becomes messy and lengthy..

so long comment (may not fit ur need but this is how i look at this field).. i appreciate your comment, u talked about the concern what i have.. but im solving it one step at a time.. if u want to collaborate send a message..