Advanced Topics and Customization
This page explores advanced capabilities of the Azure AI Evaluation SDK, including how to extend its functionality, optimize performance, and ensure compliance with enterprise standards.
1. Extending the SDK
- Custom Evaluators:
- Implement your own metric logic by subclassing BaseEvaluator
- 📘 Custom Evaluators Guide
- Custom Preprocessors:
- Add domain-specific preprocessing steps before evaluation
- Plugin Architecture:
- Register new components via configuration or code
2. Performance Optimization
- Batch Processing:
- Evaluate large datasets in chunks to reduce memory usage
- Parallel Execution:
- Use multi-threading or distributed compute via Azure ML
- Caching:
- Avoid redundant model calls by caching predictions
3. Logging and Monitoring
- Built-in Telemetry:
- Track evaluation progress and metrics in real-time
- Azure Monitor Integration:
- Export logs and metrics to Azure Monitor or Log Analytics
- 📘 Monitoring Azure ML Pipelines
4. Security and Compliance
- Data Privacy:
- Ensure sensitive data is anonymized before evaluation
- Access Control:
- Use Azure role-based access control (RBAC) for secure operations
- Audit Trails:
- Maintain logs of evaluation runs for compliance reviews
- 📘 Azure Security Best Practices
5. Deployment Considerations
- CI/CD Integration:
- Automate evaluation workflows in DevOps pipelines
- Model Registry Hooks:
- Trigger evaluations when new models are registered
- 📘 Azure ML Model Registry