The text explores the transformative impact of AI-driven computer vision on quality control within the manufacturing industry, highlighting its ability to automate defect detection with unprecedented accuracy and efficiency. Traditional manual inspections, which were often inconsistent and unscalable, are being replaced by AI-powered systems that utilize machine learning to identify defects such as surface scratches and material inconsistencies more rapidly and precisely. The success of these systems hinges on high-quality data collection and preparation, including the use of object detection models and Large Vision Models (LVMs). While object detection models are cost-effective solutions for well-defined defect types, LVMs offer flexibility and adaptability for more complex quality control challenges. The text also discusses the importance of data quality, standardization, and annotation in optimizing model performance, and highlights Clarifai's role in offering robust solutions for implementing AI-driven defect detection. Clarifai provides tools for training custom models and deploying them efficiently using Compute Orchestration, which ensures scalable and cost-effective access to advanced AI models across various manufacturing applications.