From Data to Decisions: A Domain-Driven Path to Industrial AI

Artificial Intelligence used in the real industry often faces a fundamental gap when data scientists create shiny models in a laboratory setting. Still, domain experts and operators need a system they can trust and rely on in production.

In this session, it is explored how Domain-Driven Design (DDD) can be applied to design robust and interpretable AI architectures for complex industrial use cases.

Based on a real-world case study it is demonstrated how bounded contexts, ubiquitous language, and domain events transform the AI pipeline into a production system that communicates in the same language as domain specialists and quality managers.

Lernziele

Attendees will learn how the DDD principles create a solid architecture, spanning from image acquisition to defect detection, handling root causes, and generating reports that provide traceability, clarity, and a clear linkage between each stage and business goals, such as quality, safety, and cost reduction.