Overview
ML optimization systems connect experiment design with operational constraints: latency, cost, quality, and maintainability.
Problem
Teams can produce experiments faster than they can interpret or operationalize them.
Constraints
- Metrics need to map to product outcomes.
- Serving costs should be visible during design.
- Experiments must be reproducible enough to compare.
System Design
The loop captures dataset slices, prompt or model variants, run metadata, evaluation results, and deployment notes in a single artifact trail.
Architecture
Experiment jobs write structured results to a lightweight registry. Dashboards compare quality and cost across model and retrieval configurations.
Tradeoffs
The system avoids heavyweight platform assumptions, accepting fewer features in exchange for rapid iteration.
Impact
The output is a practical path from prototype quality to production tradeoff.
What I Learned
Optimization is only useful when the team can name what got better and what got worse.
Research Extension
Study small models as routing, compression, and critique layers around larger models.