Introduction to Artificial Intelligence
- Introduction to AI
Quality Characteristics for AI-Based Systems
- Quality characteristics for AI-based systems
- Acceptance criteria for AI-based systems
Machine Learning
- Introduction to machine learning
- Data for machine learning
- ML functional performance metrics for classification
- Neural networks
Testing AI-Based Systems
- Introduction to testing AI-based systems
- Testing generative AI and large language models
- Test levels and machine learning systems
Input Data Testing for Machine Learning Systems
- Input data testing for machine learning systems
Model Testing for Machine Learning Systems
- Model testing for machine learning systems
Machine Learning Development Testing
- Machine learning development testing
Learning Outcomes
- Understand the current state of AI, including generative AI
- Experience the implementation and testing of machine learning models
- Understand the AI-specific quality characteristics defined by ISO/IEC 25059
- Calculate and interpret ML functional performance metrics for machine learning models
- Contribute to developing an effective test strategy for a machine learning system
- Design and execute test cases for machine learning systems
Target Audience
Not limited to testing specialists — also relevant for the following roles:
- Testers, test analysts, test engineers, test managers, test consultants
- Data analysts and data scientists
- Software developers building AI-based systems
- User acceptance testers
- Project managers, quality managers, software development managers
- Business analysts, IT directors, management consultants
Prerequisites
ISTQB® Certified Tester Foundation Level certification is required.
Course Duration: 2 days
This certification builds knowledge of how to test AI and deep-learning systems — particularly machine-learning-based systems and generative AI (LLM) systems. It covers test approaches specific to AI systems, including probabilistic behavior, non-determinism, and dependence on data. The syllabus follows a lifecycle-based approach, bringing together input data testing, model testing, machine learning development testing, and the quality characteristics specific to modern AI-based systems. The v2.0 release also adds current content on testing generative AI and large language models, making it relevant not only for professionals working with classic machine-learning models but also for those working with today’s LLM-based systems.