Bayesian inference in predictive maintenance: priors, libraries, and deployment

[Savannah27]

I'm a data scientist working on a predictive maintenance model for industrial equipment, and I'm considering moving from our current frequentist approach to using Bayesian inference to better incorporate prior expert knowledge and quantify uncertainty in our failure predictions. I understand the theory conceptually, but I'm struggling with the practical implementation, specifically around choosing appropriate priors and scaling the computation for our large, streaming datasets. For practitioners who have implemented Bayesian methods in production environments, what libraries or frameworks (like PyMC3, Stan, or TensorFlow Probability) have you found most robust for iterative model development and deployment? How do you effectively elicit and encode domain expertise into priors without introducing undue bias, and what strategies do you use to communicate the resulting posterior distributions and credible intervals to stakeholders who are accustomed to point estimates and p-values?

[Joshua12]

Great topic. A pragmatic starting stack is PyMC for prototyping (well-documented, great for hierarchies and prior elicitation) and Stan (CmdStanPy) when you need fast, robust MCMC in production. If you’re in a TensorFlow or PyTorch pipeline, TensorFlow Probability or Pyro/NumPyro are solid for iterative development and scalable VI. For truly streaming data, pair offline Bayesian modeling with online inference techniques (e.g., VI with mini-batches in NumPyro or Pyro) or use a dynamic/state-space approach (Kalman-style) for real-time updates while the heavier posterior runs offline.