I'm struggling to apply this model to code for machine learning experiments. The functional core takes an extremely long time to compute, e.g. "train the model"

I'm struggling to apply this model to code for machine learning experiments. The functional core takes an extremely long time to compute, e.g. "train the model". Thus, you want to save lots of intermediate values to disk so you can reload them over and over while you are experimenting the downstream part of the program. I end up with a lot of code writing values to the disk, which is currently mixed in with the computations. I'm wondering if there's some way to automate this "save intermediate values to the disk" so I can write the code in a more functional style without having to constantly go in and out of the imperative shell portion. The process of ML experimentation is extremely painful compared to normal software development where nothing really takes that long to compute.