What is probabilistic model serving and why is it challenging in production?

Probabilistic model serving returns uncertainty-aware predictions instead of single deterministic outputs.

Unlike deterministic models, probabilistic models output distributions (mean, variance, confidence intervals). Serving such models requires careful handling of sampling, latency constraints, and calibration. In production, storing and transmitting uncertainty metrics increases payload size and requires downstream systems to interpret probabilistic outputs correctly.