Data science development is an experimental and iterative process. It involves a lot of trial and error and it's easy to lose track of what's been tested and what hasn't. The following examples show how Prodmodel — an open-source data engineering tool I developed — helps to solve some of those problems. It works with Python 3.5 or above.
The idea behind Prodmodel is to structure your modeling pipeline as Python function calls. The tool then versions, caches, and reuses the objects returned by these functions. This way you don't have to keep in mind the various data or model files or pieces of codes you're experimenting with.