This program equips QA teams and test engineers with skills to audit AI output. Focus: quality, robustness, reproducibility. Learn to measure precision, recall, and validate model performance. Enhance explainability and manage model lifecycle quality.
Audience: QA teams, test engineers.
Learning Objectives:
- Measure and audit AI output quality.
- Plan effective regression tests.
- Validate model explainability and performance.
- Apply metrics for precision and recall.
- Implement quality gates in model lifecycles.
- Ensure AI robustness and reproducibility.
Program Modules:
- AI Quality Metrics:
- Precision and Recall.
- Calibration techniques.
- F1-score application.
- Error rate analysis.
- ROC curve interpretation.
- AUC evaluation.
- Regression Testing for AI:
- Test planning strategies.
- Data drift detection.
- Model version control.
- Automated testing frameworks.
- Performance baselines.
- Change impact analysis.
- Explainability Validation:
- SHAP values.
- LIME explanations.
- Feature importance.
- Bias detection methods.
- Model transparency.
- Interpretability metrics.
- Performance Validation:
- Latency measurement.
- Throughput analysis.
- Resource utilization.
- Scalability testing.
- Stress testing.
- A/B testing.
- Model Lifecycle Quality Gates:
- Deployment criteria.
- Monitoring strategies.
- Feedback loops.
- Retraining triggers.
- Version management.
- Risk assessment.
- AI Robustness and Reproducibility:
- Adversarial testing.
- Data augmentation.
- Seed management.
- Environment control.
- Consistency checks.
- Failure mode analysis.
Exam Domains:
- Performance Evaluation Protocols.
- Model Reliability Frameworks.
- Data Integrity and Validation.
- Algorithmic Bias Assessment.
- Operational Deployment Standards.
- Quality Assurance Methodologies.
Course Delivery:
The course is delivered through lectures and interactive discussions. Online resources provide readings and case studies.
Assessment and Certification:
Participants are assessed via quizzes and assignments. Successful completion grants AIQSC certification.
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions
- Short Answer Questions
Passing Criteria: Candidates must achieve 70% or higher to pass.
Enroll now to enhance your AI quality assurance expertise.