Comprehensive testing frameworks for AI systems including behavioral testing, metamorphic testing, unit tests for ML, and drift detection. Ensure model quality through systematic validation.
Sentiment analysis testing, NER validation, text classification QA
Object detection testing, image classification validation, OCR quality
Credit model validation, fraud detection testing, risk model QA
Diagnostic model validation, clinical NLP testing, imaging AI QA
Perception testing, decision model validation, safety verification
Define model capabilities and create test cases for each using CheckList methodology
Implement MFT, INV, and DIR tests for minimum functionality and invariance
Define metamorphic relations specific to your domain and model type
Create unit tests for data pipelines, feature engineering, and model behavior
Set up monitoring for data drift and concept drift with automated alerts
Integrate tests into ML pipelines with quality gates and regression checks
| Component | Function | Tools |
|---|---|---|
| Behavioral Testing | Capability-based test matrices (MFT, INV, DIR) | CheckList, TextAttack, SYNTHEVAL |
| Metamorphic Testing | Test without ground truth using input-output relations | MeTMaP, custom frameworks |
| Unit Testing | Data pipeline, feature, and model component tests | pytest, Great Expectations |
| Drift Detection | Data drift and concept drift monitoring | Evidently AI, WhyLabs, NannyML |
| Regression Testing | Ensure model updates don't degrade performance | Custom baselines, golden sets |
| Test Orchestration | CI/CD integration, quality gates | GitHub Actions, Jenkins, MLflow |
Let us help you implement comprehensive AI testing frameworks that ensure model quality.
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