The PCB Defect Detector is an advanced machine learning application designed to identify and classify defects in printed circuit boards (PCBs during the manufacturing process. By leveraging computer vision and artificial intelligence, it automates the inspection of PCBs, ensuring high-quality standards and reducing reliance on manual inspections. Key Features and Functionality: - Automated Defect Detection: Utilizes machine learning models to detect various PCB defects, including missing components, soldering issues, and surface anomalies. - High Accuracy: Employs advanced algorithms to achieve precise identification of defects, minimizing false positives and negatives. - Scalability: Capable of handling high volumes of PCB inspections, making it suitable for large-scale manufacturing operations. - User-Friendly Interface: Features an intuitive interface that allows operators with minimal technical knowledge to effectively use the system. - Integration with AWS Services: Seamlessly integrates with AWS services such as Amazon SageMaker and AWS Lambda for model training, deployment, and inference. Primary Value and Problem Solved: The PCB Defect Detector addresses the challenges of manual PCB inspections, which are often time-consuming and prone to human error. By automating the defect detection process, it enhances inspection accuracy, reduces operational costs, and accelerates production cycles. This leads to improved product quality and increased customer satisfaction, while also allowing manufacturers to allocate human resources to more complex tasks.