Selected keynotes, invited sessions, and engineering talks on data quality, AI reliability, large-scale data platforms, and operational trust.
Keynote on enterprise AI architecture, LLMOps, observability, and the controls required beyond model quality.
Key takeaway: Reliable AI requires architecture, evidence, ownership, and recovery paths beyond the model.
View keynote slides ↗A practitioner session on anomaly detection, observability, and governance patterns that surface silent data failures before they reach models and decisions.
Key takeaway: Schema checks are not enough when data remains structurally valid but semantically wrong.
Official DAMA Georgia event ↗A technical session on temporal drift, data quality, and operational failure modes in production AI systems.
Key takeaway: Production AI failures often begin in data and time semantics rather than in the model itself.
View technical-session slides ↗An invited technical talk connecting real-time data platforms, dependable pipelines, and GenAI-enabled experiences.
Key takeaway: Real-time AI experiences depend on dependable data movement, context, and operational controls.
View invited-talk slides ↗