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Forecasting Track

This track focuses on time-aware demand forecasting and API observability for forecast consumption.

  1. projects/nyc-demand-forecasting-foundations-showcase
  2. projects/demand-api-observability-showcase
  3. projects/mlops-drift-production-showcase (optional extension)

Core Skills Covered

  • Chronological train/val/test splitting.
  • Time feature engineering for demand prediction.
  • Forecast metrics interpretation (MAE, RMSE, sMAPE).
  • Exposing forecasting models through monitored APIs.

Evidence Artifacts To Inspect

  • artifacts/splits/time_split_manifest.json
  • artifacts/eval/metrics_summary.csv
  • artifacts/eval/prediction_examples.csv
  • artifacts/metrics.json in API observability showcase
  • /metrics endpoint counters and latency histograms

Suggested Reflection Prompts

  • Which forecast error metric best reflects product risk?
  • How would you detect seasonal drift in near real time?
  • Which API metrics should block rollout to production?