Showcase Architecture

This note maps related showcases into cohesive, in-repo learning tracks.

Why this architecture

• Keep each showcase focused on one learning outcome.
• Preserve reproducibility and short demo runtime.
• Avoid monolithic project structure for students.

Ranking Track

1. projects/learning-to-rank-foundations-showcase
2. Grouped ranking data preparation and relevance labeling.
3. LambdaRank model training.

4. NDCG-focused evaluation and split artifacts.

5. projects/ranking-api-productization-showcase

6. FastAPI ranking endpoints (/health, /model/schema, /score, /rank).
7. Model artifact loading and schema-safe scoring.
8. Structured request logging and OpenAPI export workflow.

Forecasting And Observability Track

1. projects/nyc-demand-forecasting-foundations-showcase
2. TLC-style hourly aggregation and time feature engineering.
3. Explicit time-ordered train/val/test split.
4. Demand forecasting metrics (MAE, RMSE, sMAPE).

5. Optional real TLC download path with synthetic default mode.

6. projects/demand-api-observability-showcase

7. FastAPI demand serving endpoint (/predict) and health checks.
8. Prometheus metrics endpoint (/metrics) and request latency counters.
9. Optional OpenTelemetry instrumentation hooks.
10. OpenAPI export/check and API behavior tests.

Intentional Scope Boundaries

• Full-size raw datasets are excluded to keep clone and run workflows lightweight.
• Large generated caches are excluded from version control.
• Each showcase keeps only teaching-critical components and artifacts.