Libraries and Priors for Scalable Bayesian Inference in Production Analytics

[KevinG]

I'm a data scientist working on a project where we need to quantify uncertainty in our model predictions, and I'm exploring Bayesian methods as an alternative to our standard frequentist approaches. I understand the theory of updating priors with data, but I'm struggling with the practical implementation, specifically choosing appropriate priors for real-world business data and computationally efficient ways to perform inference. For practitioners who have integrated Bayesian statistics into production pipelines, what libraries or frameworks have you found most robust for large-scale problems, and how do you effectively communicate the results, like credible intervals, to stakeholders accustomed to p-values and confidence intervals?

[Brandon_P]

Two quick takeaways for production-style Bayesian work: (1) use weakly informative priors and, when you have groups, hierarchical priors to borrow strength across segments; (2) for scale, pick probabilistic programming tooling that supports both fast approximate inference and exact MCMC, then pick the path that fits your latency needs. Tools like NumPyro (JAX) or PyMC (Theano/PyMC) work well for Python stacks, Stan via CmdStanPy is rock-solid for robust HMC, and Pyro or TensorFlow Probability can help if you’re already in PyTorch or TF ecosystems.