ML Pipeline Testing Framework

Create a testing framework for ML pipelines.

Pipeline components to test:
- Data processing: [PREPROCESSING STEPS]
- Feature engineering: [FEATURE TRANSFORMS]
- Model training: [TRAINING PIPELINE]
- Inference: [PREDICTION PIPELINE]

Testing requirements:
1. Data tests:
   - Schema validation
   - Distribution checks
   - Missing value handling
   - Edge cases
2. Feature tests:
   - Transform correctness
   - Feature ranges
   - Encoding consistency
3. Model tests:
   - Training convergence
   - Metric thresholds
   - Reproducibility
4. Integration tests:
   - End-to-end pipeline
   - API contracts
   - Performance benchmarks
5. Regression tests:
   - Model comparison
   - Performance degradation
   - Behavioral tests
6. Infrastructure:
   - pytest fixtures
   - Test data generation
   - CI integration
7. Monitoring tests:
   - Alert validation
   - Dashboard accuracy

Use Great Expectations, pytest, etc.