Showcase · AI & Machine Learning

Model Evaluation Harness

A benchmark harness that makes model comparisons reproducible: tracked datasets, fixed seeds and per-slice error reporting by default.

Discipline
AI & Machine Learning
Role
Applied machine learning
Duration
6 weeks
Year
2026

Problem

Comparisons between runs were untrustworthy because dataset versions, preprocessing and seeds drifted quietly between experiments.

Approach

  1. 01Pinned dataset versions by content hash and refused runs on unpinned data.
  2. 02Recorded the full environment and seed set alongside every result.
  3. 03Reported per-slice error and seed variance as first-class outputs.
  4. 04Made every published number regenerable with a single command.