ISTQB AI Testing: ML Model Creation and Validation Explained

Certification study starts with a question testers cannot skip: how do you know a machine learning system will behave reliably outside a controlled lab? This episode breaks down the ISTQB AI testing view of model creation and validation, from data preparation to the final proof of accuracy. The lesson maps the main forms of machine learning, walks through the structured ML creation workflow, and explains why functional performance criteria and metrics must be defined early. It also shows how pre-trained models and retrieval augmented generation change the testing scope, then connects these ideas to a supervised classification challenge and the difference between evaluating and testing a model. In this episode: • Review supervised, unsupervised, and reinforcement learning as testing blueprints • Follow data preparation, algorithm selection, training, evaluation, and testing • See how fine-tuning and RAG introduce new verification challenges • Understand why validation data tunes a model while test data proves it CHAPTERS 0:00 Why AI testing needs proof 0:52 Forms of machine learning 1:44 ML creation workflow and metrics 2:36 Pre-trained models and RAG 4:21 Supervised classification challenge 6:03 Evaluation versus testing #ISTQBAItesting #AItesting #machinelearningtesting #MLmodelvalidation