2.0 KiB
2.0 KiB
Production ML Pipeline
Objective: Get the plant disease identification ML pipeline to full production readiness with real model inference, proper class mapping, and production-grade error handling.
Status legend: [ ] todo, [~] in-progress, [x] done
Tasks
- 01 — PlantVillage class inventory and knowledge base mapping →
01-plantvillage-class-inventory.md - 02 — Label mapping layer implementation →
02-label-mapping-implementation.md - 03 — TensorFlow.js model loading verification and fixes →
03-model-loading-verification.md - 04 — Confidence calibration for PlantVillage model →
04-confidence-calibration.md - 05 — Real model integration into identification pipeline →
05-pipeline-integration.md - 06 — Plant-context-aware identification →
06-plant-context-identification.md - 07 — End-to-end integration testing →
07-end-to-end-testing.md - 08 — Production hardening and observability →
08-production-hardening.md
Dependencies
- 01 → 02 (mapping data feeds label layer)
- 02 → 05 (labels feed pipeline)
- 03 → 05 (verified model loading feeds pipeline)
- 04 → 05 (calibration feeds pipeline)
- 05 → 06 (real model enables plant context)
- 05 → 07 (integrated pipeline enables e2e testing)
- 07 → 08 (tested pipeline enables production hardening)
Exit Criteria
- The feature is complete when:
- Model loads successfully and produces real (non-mock) predictions
- All 38 PlantVillage classes map to valid knowledge base disease IDs
- End-to-end pipeline works: upload image → get real disease diagnoses with calibrated confidence
- Confidence scores are meaningful (high confidence for clear cases, low for ambiguous)
- Plant context optionally boosts relevant predictions
- Full integration test suite passes
- Error handling, logging, and monitoring in place
- No demo mode fallback in production
- Rate limiting and input sanitization active
- Health endpoint reports model status and inference metrics