Artificial Intelligence, particularly deep learning and machine vision, is transforming production and manufacturing by automating visual inspection processes, which traditionally relied on manual inspection and were prone to human error. Visual inspection is crucial in assessing product quality and identifying defects, with applications spanning industries like nuclear power, pharmaceuticals, and food. Manual inspections, despite their flexibility and human touch, are limited by high error rates and costs. Automated visual inspections leverage deep learning algorithms and machine vision to surpass human capabilities by offering faster, more accurate, and reliable assessments of products, even in hazardous environments. Deep learning models, with their ability to learn from examples and improve over time, are essential to these systems, enabling them to detect minute defects and variations in product appearance. This trend is part of the broader Industry 4.0 revolution, characterized by smart factories and cyber-physical systems, which aim to enhance efficiency and productivity in manufacturing. Companies like Nanonets provide tools to develop and deploy custom deep learning models for automated visual inspection, facilitating this shift towards automation and improving quality control across various sectors.