This whitepaper presents a structured validation strategy for AI/ML models used in vision-based quality control within Industry 4.0 manufacturing environments. It explains how AI-powered computer vision systems—leveraging technologies like NVIDIA Jetson edge devices and deep learning models—enable real-time defect detection, improving production accuracy (up to ~98%) and reducing manual inspection errors. The paper emphasizes a hybrid validation approach combining real-world and simulated (in-lab) environments to ensure robust testing. Key validation parameters include data quality, model accuracy, performance, robustness, explainability, fairness, and generalizability. It also outlines a test automation framework using Python and CI/CD integration to continuously validate models. The stra

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