Use case of Google Colab over Jupyter Notebook?

I am in a non-programming job, and I don't want to install everything on my work computer to get it set up for Jupyter. With Google Colab, I can just start working without having to do any installation and share my scripts with non-technical co-workers who wouldn't be able to install anything themselves.

My reasons for using Colab

• Work from any computers. All notebooks are saved in Google Drive.
• Don't need to worry that conda create env will clutter your directories
• Share to someone, or everyone easily. Just like a Google Doc.
• Automatic history and versioning
• Free GPU (and TPU as of 2018-09-29)
• Form widgets are simple and easier to use
• Good defaults
  • %matplotlib inline not needed
  • collapsible outline (no need to install)
  • Cleaner interface (I prefer it than vanilla Jupyter)

As the name suggests, Google Colab comes with collaboration backed in the product. It also runs on Google servers and you don’t need to install anything. Moreover, the notebooks are saved to your Google Drive account.

If you’re just playing around or working on personal projects, Jupyter will work fine. If you want to build commercial-grade models and deploy them to production, Codelab provides the full-lifecycle approach that you’d need.

Saves to Google Drive which allows you to also share and have multiple people work on the same document at once.Collapsible section and section tree. Interactive widgets like sliders.

Scratch cell: A cell to run test code but which isn’t saved in your notebook. Code snippets

pdb debugger support

However the runtime is a lot more restrained as Jupyter is just a web UI to a machine on which you may actually install anything you need

Google Codelab is a full-lifecycle workbench that helps you build/deploy/scale your models in a way that notebooks alone can’t.

With Codelab, you can prototype your model locally with scikit-learn or TensorFlow and then move it to/train it in the cloud with a huge dataset.

While Jupyter is the open source project on which Colaboratory is based. Colaboratory allows you to use and share Jupyter notebooks with others without having to download, install, or run anything on your own computer other than a browser.

On top of that, it has a whole selection of libraries for interacting with other cloud services, especially BigQuery.

Better still, you have 12 hours continuous access to a GPU for free! This really comes in handy for dappling in data Science especially in under resourced communities.