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.