Lilly Tech Systems
AI Testing & QA
Master the art and science of testing AI and machine learning systems. Learn to validate ML models, ensure data quality, build robust integration tests, monitor production systems, and apply industry best practices for AI quality assurance.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why AI testing differs from traditional software testing, unique challenges, and the testing pyramid for ML systems.
2. Testing ML Models
Unit tests for models, performance benchmarks, regression testing, A/B testing, and evaluation metrics validation.
3. Data Testing
Data validation, schema testing, distribution drift detection, data quality checks, and pipeline integrity verification.
4. Integration Testing
End-to-end pipeline testing, API contract tests, service integration, model serving validation, and system-level QA.
5. Monitoring
Production model monitoring, drift detection, alerting systems, performance dashboards, and observability for AI.
6. Best Practices
CI/CD for ML, test automation strategies, documentation standards, team workflows, and building a testing culture.
What You'll Learn
By the end of this course, you'll be able to:
Validate ML Models
Design and implement comprehensive test suites for machine learning models across training, evaluation, and deployment stages.
Ensure Data Quality
Build automated data validation pipelines that catch schema violations, distribution drift, and data corruption early.
Test End-to-End
Create integration tests that verify the entire ML pipeline from data ingestion through model serving and response delivery.
Monitor Production
Set up monitoring, alerting, and observability systems that detect model degradation and data issues in real time.