docs: record final validation completion pass

README.md

@@ -83,6 +83,7 @@ YOLOv8n was validated with a real OpenCV image-input benchmark: InferEdgeRuntime
 TensorRT Jetson was 4.6x faster than ONNX Runtime CPU in this real image input benchmark.
 The benchmark uses end-to-end Runtime latency, not trtexec GPU-only latency.
 The full pipeline portfolio summary is available at [docs/portfolio/inferedge_pipeline_portfolio.md](docs/portfolio/inferedge_pipeline_portfolio.md), and the detailed Runtime comparison report is available at [docs/portfolio/runtime_compare_yolov8n.md](docs/portfolio/runtime_compare_yolov8n.md).
+The final local-first validation completion pass is summarized in [docs/portfolio/final_validation_completion.md](docs/portfolio/final_validation_completion.md).
 The YOLOv8 COCO subset accuracy demo is documented in [docs/portfolio/yolov8_coco_subset_evaluation.md](docs/portfolio/yolov8_coco_subset_evaluation.md).
 Validation problem cases are documented in [docs/portfolio/validation_problem_cases.md](docs/portfolio/validation_problem_cases.md).
 

docs/portfolio/final_validation_completion.md

@@ -0,0 +1,79 @@
+# InferEdge Final Validation Completion Pass
+
+This document records the current completion state for the portfolio-grade InferEdge validation workflow.
+It does not claim production SaaS readiness.
+
+## Completion Definition
+
+InferEdge is complete for the current portfolio milestone when it can replay a local-first validation workflow with:
+
+- build/provenance responsibility separated from Runtime execution
+- Runtime-compatible result evidence
+- Lab comparison and deployment decision ownership
+- optional AIGuard diagnosis evidence
+- contract/preset-based evaluation
+- normal and problem demo evidence
+- JSON/Markdown/HTML report artifacts
+- Local Studio browser replay
+- full test suite passing
+
+## Completed Evidence
+
+| Area | Status | Evidence |
+|---|---|---|
+| Runtime evidence | done | ONNX Runtime CPU and TensorRT Jetson demo result fixtures |
+| Compare evidence | done | `compare_key` / `backend_key` grouped runtime comparison |
+| Local Studio | done | Run, Import, Jetson helper, Job/Result, Compare, Decision, Demo Evidence |
+| `yolov8_coco` preset | done | `inferedgelab/validation/presets.py` |
+| `model_contract.json` | done | `examples/validation_demo/yolov8_coco_model_contract.json` |
+| COCO annotation loading | done | `inferedgelab/validation/coco.py` |
+| Structural validation | done | bbox/score/class validation helpers and tests |
+| Accuracy report | done | YOLOv8 COCO subset report with mAP@50, precision, recall |
+| Normal demo case | done | `examples/validation_demo/subset/` |
+| Problem demo cases | done | annotation missing, invalid structure, contract mismatch reports |
+| Report formats | done | JSON, Markdown, HTML evaluation reports |
+| Tests | done | full `pytest` suite passing locally |
+
+## Validated Numbers
+
+Runtime demo pair:
+
+- ONNX Runtime CPU: 45.4299 ms mean / 49.2128 ms p99 / 22.0119 FPS
+- TensorRT Jetson: 9.9375 ms mean / 15.5231 ms p99 / 100.6293 FPS
+- Studio speedup display: about 4.57x faster
+
+YOLOv8 COCO subset evaluation:
+
+- Samples: 10
+- Ground-truth boxes: 89
+- mAP@50: 0.1410
+- mAP@50-95: 0.0873
+- Precision: 0.2941
+- Recall: 0.1685
+- F1 score: 0.2143
+- Structural validation: passed
+
+## Problem Evidence
+
+| Case | Expected Signal |
+|---|---|
+| annotation missing | review |
+| invalid detection structure | blocked |
+| contract shape mismatch | blocked |
+
+## Remaining Future Work
+
+These are intentionally outside the current completion boundary:
+
+- production worker daemon
+- persistent database or queue
+- file upload product flow
+- production frontend deployment
+- authentication, billing, and multi-user controls
+- full COCO official evaluation
+- more presets such as `resnet_imagenet`
+
+## Portfolio Message
+
+InferEdge is a local-first, contract/preset-based edge AI inference validation pipeline.
+It shows how latency, accuracy, output structure, provenance, and deployment decision evidence can be connected without claiming arbitrary automatic model evaluation or production SaaS completeness.

docs/portfolio/inferedge_pipeline_status.md

@@ -7,6 +7,7 @@ This document summarizes the current portfolio status of the InferEdge multi-rep
 InferEdge is an end-to-end edge AI inference validation pipeline. It is designed to turn an ONNX model into deployment evidence by connecting artifact build provenance, runtime profiling, Lab comparison/reporting, optional rule-based diagnosis, and a final deployment decision.
 
 For a compressed recruiter/interviewer entry point, see [InferEdge 1-Page Architecture Summary](inferedge_1page_architecture.md).
+For the current portfolio completion checkpoint, see [InferEdge Final Validation Completion Pass](final_validation_completion.md).
 
 Interview summary:
 
@@ -112,6 +113,9 @@ Demo readiness: `scripts/demo_pipeline_full.sh` now provides a guided end-to-end
 
 ## Implemented vs Planned
 
+Current milestone: **portfolio-grade local-first validation workflow complete**.
+This does not mean production SaaS is complete.
+
 ### Implemented Now
 
 - Structured benchmark/result comparison in Lab