Kaggle notebook Vs Google Colab What are the major differences between Kaggle notebook and Google Colab notebook?
To work on a dataset my first step is to start a Kaggle notebook but then I cant help thinking what could be the advantage of using Colab notebook instead.
I know few differences, correct me if I'm mistaken about any:

Kaggle has a console and Colab doesn't (but I still don't know what to do with the console).
Kaggle notebook allows collaboration with other users on Kaggle's site while Colab allows collaboration with anyone using the notebook's link.
Kaggle doesn't have &quot;Stackoverflow&quot; instant search like Colab does.
Kaggle requires uploading the data onto the Kaggle's site while Colab allows using data from Drive.
Kaggle doesn't have the feature of uploading the notebook directly to GitHub like Colab does.
Kaggle's notebook can be made public for all the users in Kaggle to view,vote,fork and discuss but Colab's notebook remains in your Drive until you decide to share with someone or upload somewhere.

Anything to add that is technically advantageous?
There are several rundowns between the two services like this one.
Key for me:

Only Kaggle supports R, only Colab supports SWIFT
Colab is a Google product and is therefore optimized for Tensorflow over Pytorch
Colab is a bit faster and has more execution time (9h vs 12h)
Yes Colab has Drive integration but with a horrid interface, forcing you to sign on every notebook restart

Kaggle has a better UI and is simpler to use but Colab is faster and offers more time.