Aligned with
Check your data
before testing your model
Data quality
6 passing2 failing
- Data Integrity: 88/100
- Completeness: 91/100
- Validity: 28/100
- Consistency: 22/100
- Uniqueness: 84/100
- Representativeness: 76/100
- Leakage Risk: 90/100
- Redundancy: 82/100
Data Integrity
88/100Pass
Completeness
91/100Pass
Validity
28/100Fail
Consistency
22/100Fail
Uniqueness
84/100Pass
Representativeness
76/100Pass
Leakage Risk
90/100Pass
Redundancy
82/100Pass
Test how your model
handles real conditions
Test scenarios
- Baseline
Supported models
Tabular
ClassificationRegression
Vision
ClassificationDetectionSegmentationOBB
Review status
Ready
Test metrics
- Accuracy
- F1
- AUROC / AUPR
- mAP 50:95
- mIoU
- RMSE / R2
Track risks and how you address them
| Risk ID | Finding | Status | Level |
|---|---|---|---|
| R-42001-014 | No owner assigned to AI management | Inherent | High |
| R-23894-021 | Fairness changes across groups | Mitigated | Medium |
| R-TS42119-006 | Missing evaluation records | Residual | Medium |
| R-QA-088 | Sensitive to corrupted data | Mitigated | Low |
R-42001-014
No owner assigned to AI management
Level: High
R-23894-021
Fairness changes across groups
Level: Medium
R-TS42119-006
Missing evaluation records
Level: Medium
R-QA-088
Sensitive to corrupted data
Level: Low
Edison AwardsSee testing in practice
Case study
InspecSpider high-mast inspection
- Real inspection footage
- Robustness testing
- Documented results