Operations¶

bijux-proteomics-intelligence operations is about changing judgment without making it arbitrary. Maintainers here are not just shipping code. They are shipping policy behavior, recommendation quality, and explanation patterns that people may use to decide what gets advanced, redesigned, or paused.

flowchart LR
    policy["policy or evaluator change"]
    scenarios["rerun scenario and ranking tests"]
    explain["check explanation and brief quality"]
    drift["inspect portfolio and design-loop drift"]
    review["review recommendation consequences"]
    release["publish updated judgment surface"]

    policy --> scenarios --> explain --> drift --> review --> release

What Operations Means Here¶

recommendation drift is an operational concern, not just a modeling concern
a passing test suite is incomplete if explanations become harder to trust
maintainers need to reason about output quality across scenarios, not only single-function correctness

Start With¶

open Common Workflows when you need the standard path from policy edit to trustworthy release
open Observability and Diagnostics when rankings, briefings, or scenario outputs no longer look believable
open Failure Recovery when recommendation behavior already regressed in a way humans can see
open Release and Versioning before publishing any change that alters policy defaults or explanation shape

Route From Operating Concern¶

Local Development and Installation and Setup for reproducible scoring and evaluation work
Deployment Boundaries and Security and Safety for the limits that stop recommendation logic from becoming hidden authority
Performance and Scaling when evaluation volume, portfolio breadth, or report generation cost becomes the practical bottleneck

First Proof Check¶

src/bijux_proteomics_intelligence/policies.py and evaluators.py
src/bijux_proteomics_intelligence/report/ and outcomes.py
packages/bijux-proteomics-intelligence/tests