Designing BayesPilot: from model metrics to deployment decisions
BayesPilot treats model promotion as an explicit decision workflow, not just a leaderboard of scores.
Why this problem matters
In many ML projects, the pipeline ends at validation metrics. But production rollout requires a different question: is this model safe and useful to operate under real constraints? A model can improve AUC while still failing calibration, creating unstable threshold behavior, or producing decisions that are hard to justify later.
BayesPilot is designed around that gap between experiment success and deployment readiness.
What BayesPilot is organizing
BayesPilot links each stage of a production-style workflow into one traceable chain: training run metadata (data slice, feature set, and model config), evaluation outputs, calibration artifacts, operating-threshold selection, and final promotion decision.
Instead of keeping metrics as disconnected artifacts, the system records why a candidate was accepted or rejected, including the baseline it was compared against and the go/no-go criteria applied at that time.
Key design choices
Decision gates, not single scores. Promotion is blocked unless baseline comparison, calibration quality, and threshold behavior all pass defined checks.
Calibration as a first-class step. BayesPilot separates ranking quality from probability quality, so thresholds are selected from calibrated probabilities rather than raw model confidence.
Traceability by default. Each promotion decision is tied to the exact run, evaluation snapshot, and threshold policy used to make it, so later review and iteration are reproducible.
Why this matters in practice
The practical value is not only better predictions; it is clearer operational decision-making. Teams can revisit why a model was promoted, compare rollout logic across versions, and update thresholds without losing the decision history behind earlier releases.
The result is a release process that is repeatable by default, auditable when needed, and easier to improve over time.