R markdown has been amazing in organizing my work and packaging it for analysis-type projects. With GNU Make, packrat, and a specific folder structure the whole thing is reproducible and just so efficient to work with.
I struggle a bit with machine learning model development projects because they typically have many parts and are quite circular. Also, there are two competing packages
mlr for which there are overlapping functionality.
How do you structure a machine learning project and make it reproducible (ignore the productionization as I think that part already has a lot of work around it).