Manual Model Testing

Manual Model Testing is a software testing approach where human testers manually evaluate machine learning or AI models by interacting with them to identify issues, validate performance, and ensure they meet requirements. It involves creating test cases, executing them without automation, and analyzing outputs for accuracy, bias, or unexpected behavior. This method is crucial for assessing models in real-world scenarios where automated tests might miss nuanced or contextual errors.

Also known as: Manual AI Testing, Human-in-the-Loop Testing, Manual ML Evaluation, Ad-hoc Model Testing, Exploratory Model Testing

Why learn Manual Model Testing?

Developers should use Manual Model Testing when deploying AI/ML models in production, as it helps catch edge cases, ethical concerns like bias, and usability issues that automated tests may overlook. It is particularly valuable during model validation phases, for complex models like natural language processing or computer vision, and in regulated industries where human oversight is required to ensure compliance and safety.

Learning Resources

• docs: Google's People + AI Guidebook: Testing AI Systems
• course: Coursera: Testing and Monitoring ML Models
• tutorial: Towards Data Science: Manual Testing for Machine Learning
• video: YouTube: Manual Testing of AI Models by IBM
• book: Book: 'Hands-On Machine Learning Testing'