Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG. Below is a stylized example of a machine learning flow which is expressed as a DAG. In the end you just need to run TaskTrain() and it will automatically know which dependencies to run. Writing machine learning code as a linear series of functions likely creates many workflow problems.

Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG. Below is a stylized example of a machine learning flow which is expressed as a DAG. In the end you just need to run TaskTrain() and it will automatically know which dependencies to run. Writing machine learning code as a linear series of functions likely creates many workflow problems.