![]() ![]() ![]() One of the best features although simple is that the notebook would stop compiling your code if it spots an error. The notebook environment allows us to keep track of errors and maintain clean code. Jupyter notebook environments are now becoming the first destination in the journey to productizing your data science project. The Increasing Popularity of Jupyter Notebook Environments 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. Other players have now begun to offer cloud hosted Jupyter environments, with similar storage, compute and pricing structures. Many cloud providers offer machine learning and deep learning services in the form of Jupyter notebooks. ![]() Notebooks are becoming the de-facto standard for prototyping and analysis for Data Scientists. ![]()
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