Testing Machine Learning and GenAI Systems
Test ML models, GenAI features and RAG pipelines with a repeatable, responsible QA strategy.
- Duration
- 3 hours
- Lectures
- 33
- Level
- All Levels
- Learners
- 50+ students
- Python
- REST APIs
- LLMs
- RAG
- Responsible AI
About this course
Practical, real-world testing strategies for AI/ML and GenAI systems across the full lifecycle — from early-stage model development through post-deployment monitoring.
What you'll learn
- Validate ML model accuracy and behavior
- API automation for ML model endpoints
- Responsible AI: bias, fairness, transparency
- Test RAG pipelines and GenAI prompt stability
- Post-deployment monitoring & drift detection
What you'll build
A functional test suite for an ML model endpoint
API automation that validates model responses and schemas
A responsible-AI checklist covering bias, fairness and transparency
A prompt-stability and drift-monitoring routine
Project architecture
The pipeline you assemble across the course:
- Training / model artifact
- Model API endpoint
- Functional + API test suite
- Prompt & RAG validation
- Responsible-AI checks (bias, fairness)
- Post-deployment drift monitoring
Curriculum overview
33 lectures · 3 hours of on-demand video.
01Foundations of ML & GenAI testing
- Why AI systems break deterministic testing
- Model lifecycle and where QA plugs in
02Functional & API validation
- Validate model accuracy and behaviour
- API automation for model endpoints
03GenAI-specific testing
- Prompt stability and regression
- RAG pipeline validation: retrieval, grounding, hallucination
04Responsible AI
- Bias, fairness and transparency checks
- Documenting risk for stakeholders
05Production
- Post-deployment monitoring
- Drift detection strategy
Learner reviews
Avinash
1 year ago
Provided all the necessary knowledge for hands-on AI testing. Every topic is taught in a clear, slow way — loved that. Thanks for spending the time to create such a useful resource!
Michael
Going great. Explained in detail with practical examples. Incredibly easy to follow — really helping me understand.
FAQ
Do I need machine-learning experience?
No. The course starts from a QA engineer's point of view and explains only the ML concepts you need to test the system.
Is this hands-on?
Yes — the course works through functional, API and responsible-AI testing on real ML and GenAI behaviour rather than theory alone.
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Ready to start Testing Machine Learning and GenAI Systems?
Lifetime access, 33 lectures, and a project you can put on your CV.