Jupyter notebook vs classical IDEs : do you really have to choose ?

Since they have been invented in 2014, Jupyter notebooks have sparked many debates and controverses. For some, they are the indispensable tool if you want to build a Data Science product, as they enable you to quickly explore your data and try multiple Machine Learning models. But others argue that notebooks are a big liability for those trying to build a reliable and maintainable software as it is difficult to make them evolve and to build automatic tests on top of them.

While some people want to get rid of them and others want to improve them until they can completely replace classical IDEs, we will see in this talk that a third way exits, as it is possible to enjoy the advantages of both tools if you know how to combine them.