Computer vision is becoming a key enabler of smart manufacturing, from quality inspection to robotic automation. Yet traditional development of AI models often requires large datasets, specialized expertise, and significant time. In this talk, we explore how no-code computer vision platforms are changing this landscape allowing engineers, operators, and domain experts to build, train, and deploy models without deep AI backgrounds. We’ll look at real examples from manufacturing and robotics to show how faster iteration, simpler data workflows, and scalable deployment can move automation projects from concept to production.

This talk explores how we generate high-performance computer vision datasets from CAD—without real-world images or manual labeling. We’ll walk through our synthetic data pipeline, including CPU-optimized defect simulation, material variation, and lighting workflows that scale to thousands of renders per part. While Blender plays a role, our focus is on how industrial data (like STEP files) and procedural generation unlock fast, flexible training sets for manufacturing QA, even on modest hardware. If you're working at the edge of 3D, automation, and vision AI—this is for you!