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Explore the critical foundation of data semantics in AI systems through this 33-minute conference talk that argues for understanding data meaning as the key to building truly AI-ready systems. Learn why semantics and knowledge graphs serve as the missing layer between traditional data engineering and AI-driven pipelines, moving beyond tool-focused approaches to foundational understanding. Discover how semantic layers and knowledge graphs bridge the gap between conventional data engineering practices and modern AI workflows, enabling context-rich, explainable, and reliable AI systems. Examine practical examples demonstrating how semantics empower AI applications with enhanced context and reasoning capabilities, while understanding the transition from ad hoc experimentation to intentional, scalable architecture. Master the essential role of semantics in AI-ready data systems, explore how knowledge graphs provide context and reasoning for AI applications, and gain practical steps for implementing semantic layers in daily engineering work. Understand the pathway from experimental approaches to production-grade, intentional design that supports scalable AI infrastructure and reliable machine learning operations.
