The data science community enthusiastically adopted Jupyter Notebooks as the default research environment. With its unparalleled capabilities, Jupyter Notebooks have some drawbacks that make the journey from research to production-ready, laborious. This session will cover how we can be more production-oriented when using Jupyter Notebooks and make the transition from research to production faster.
Born out of IPython in 2014, Jupyter Notebooks have seen enthusiastic adoption among the data science community. Jupyter has become so prominent that it’s now the default environment for research.
But, are Jupyter Notebooks really the best home for data scientists to develop production-ready projects? The non-linear workflow, lack of versioning capabilities, no IDE integration, and inadequate debugging tools make it laborious to productionize a project created in a Jupyter Notebook environment.
Should we just throw our Jupyter Notebooks out the window and move to classic IDEs? Probably not – Jupyter Notebooks are, after all, a great tool that gives us superhuman abilities. We can, however, be more production-oriented when using them. How does this look in practice? That is exactly what we'll cover in this talk.