> how do you create your models and what do you mean treating them as code

we start with local Jupyter notebooks, and refactor bits of code into modules that get tested, which for our models mainly means recovering parameters from simulations, and then test them on real data, where we assess performance with LOO approximations for Bayesian models (notably PSIS) and some labeling from experts (which is not taken too seriously tbh)

> why slurm and not something like airflow

because the HPC resources we have access to are built with Slurm, which is super fast, supports DAGs of jobs, schedules our jobs reliably and quickly. I don't really want the other stuff on the Airflow feature list to be honest.