Best Practices for Jupyter Notebooks - Saturn Cloud

When it comes to data science solutions, there's always a need for fast prototyping. Be it a sophisticated face recognition algorithm or a simple regression model, having a model that allows you to easily test and validate ideas is incredibly valuable. Many data science problems out there require specially crafted solutions due to their complicated nature. This means that the data scientists working on these problems will eventually need to improvise on the issue. Not having to wait to calculate some additional feature column on the dataset every time you execute your script becomes a crucial gain in terms of productivity.