As a disclaimer I’m new to Bayesian statistics having just read McElreath’s Statistical Rethinking. (1) What sort of workflow is needed to deploy a bayesian model into production, (2) how does it differ from deploying a typical ML model, and (3) how can you accomplish these steps with PyMC3? (4) Any good resources out there for this? I know some of these are broad questions and this post is filled with other questions… I tried to number them so it’s easier for reference.
(5) I would guess if you deploy a bayesian model into production you’d need to run trace checks each time the model is retrained to check for convergence issues, is that true and is there a way to automate trace checking (I’m guessing someone would manually have to check each time)? (1 or 6) Are any other steps similar to (5) necessary in the workflow deploying a bayesian model that might not be needed in a typical ML production model?
(7) Can online learning be implemented with a Bayesian Hierarchical Regression in PyMC3? Why/why not?
(8) Are there frequentist alternatives to deploying a hierarchical regression (by hierarchical I mean with nested categorical data) as a Bayesian model? What are the advantages/disadvantages? (I’ve worked with lme4 before, but I’d say thats not very feasible in a production environment).
(9) Is variational inference the best way to scale a bayesian model in production to big data?
This might not be an appropriate way of asking so many questions on a forum, and it might not be PyMC3 focused enough, so apologies if that’s the case; I’m new to this field and I’ve had trouble finding resources.