Evaluation Systems for Production ML
A conceptual design note on defensible validation, model comparison, and error analysis. No public implementation, deployment record, or reproduced evaluation result is linked here.
This is a conceptual design note, not evidence of a deployed workbench. No implementation repository, run artifacts, or evaluation report is linked for this entry. The title describes the intended problem space, not a production-delivery claim.
Validation before comparison
A model comparison is useful only when its validation assumptions are explicit. Data splits, leakage risks, target definitions, and the intended decision should be established before interpreting aggregate metrics.
A candidate evaluation workflow
- Define the task, validation protocol, and baseline.
- Compare models under the same data and scoring conditions.
- Inspect error slices alongside aggregate metrics.
- Record inputs, configuration, and limitations so a comparison can be reviewed.
Separating evaluation logic from modeling logic can make assumptions easier to inspect. That is a design rationale, not a demonstrated improvement for this entry.
Evidence needed
A demonstrated workbench would need source code, a versioned dataset or shareable fixture, metric definitions, reproducible commands, and inspectable results. None of those is substituted by this note. The listed tools are design vocabulary rather than proof of implementation.