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.

Anomaly detection is one of computer vision's most exciting and essential challenges today. From spotting subtle defects in manufacturing to identifying edge cases in model behavior, it is one of computer vision's most exciting and crucial challenges. In this session, we’ll do a hands-on walkthrough using the MVTec AD dataset, showcasing real-world workflows for data curation, exploration, and model evaluation. We’ll also explore the power of embedding visualizations and similarity searches to uncover hidden patterns and surface anomalies that often go unnoticed.

This session is packed with actionable strategies to help you make sense of your data and build more robust, reliable models. Join us as we connect the dots between data, models, and real-world deployment—alongside other experts driving innovation in anomaly detection.