As a web developer who now is increasingly coding in python.. I sympathize with this but frankly google colab is way more accessible to me than other options. Jupyter notebook is good because it provides a quick feedback loop. Google colab is needed by a beginner like me because they have taken care of all the pytorch / cuda set up. The tutorials I access online use it so it's not easy to find a different way. All the quick and easy tutorials only demonstrate using google colab (or some other similar alternative).
I messed around with paperspace for a while, but I could not get detectron2 working in it so I went back to google colab. And my ML projects, that I have ambitions to become production apps, are still toys so for now the quick and dirty notebook will have to do. But yes, I prefer to be in my element in a traditional IDE, pushing to git, tabbing between files. I certainly have > 10 years of experience doing that, but not really for ML projects if that makes sense.
I messed around with paperspace for a while, but I could not get detectron2 working in it so I went back to google colab. And my ML projects, that I have ambitions to become production apps, are still toys so for now the quick and dirty notebook will have to do. But yes, I prefer to be in my element in a traditional IDE, pushing to git, tabbing between files. I certainly have > 10 years of experience doing that, but not really for ML projects if that makes sense.