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Engineering, Personal project
ML experimentation and regression framework
Config-driven experiments with versioned data, tracked metrics, and regression tests so model changes can't quietly get worse.
- Role
- Designed and built
- Stack
- Python, Config-driven runs, Metric tracking, Drift checks
- Config
- Versioned data
- Train run
- Tracked metrics
- Regression gate
- Runs
- Config + versioned
- Changes
- Regression-tested
Goal
Make model iteration safe: every result reproducible, every change compared against the last good run.
What it does
- Config-driven experiments, so a run can be repeated exactly.
- Data versioning and metric tracking across runs.
- Automated regression tests that flag performance drops before they ship.
- Hooks for drift monitoring.
Outcome
Less manual validation and faster, more comparable iteration cycles.