Notebooks are becoming the standard for prototyping and analysis for data scientists. Many cloud providers offer machine learning and deep learning services in the form of Jupyter notebooks. Other players have now begun to offer cloud-hosted Jupyter environments, with similar storage, compute and pricing structures. One of the main differences can be multi-language support and version control options that allow data scientists to share their work in one place.
The Increasing Popularity of Jupyter Notebook Environments
Jupyter notebook environments are now becoming the first destination in the journey to productizing your data science project. The notebook environment allows us to keep track of errors and maintain clean code. One of the best features although simple is that the notebook would stop compiling your code if it spots an error. Regular IDE’s do not stop compilation even if an error is detected and, depending on the amount of code, it can be a waste of time to go back and manually detect where the error is located.