Presented by: Changming Du, Principal, Geonamic; Yan Wang, Principal, Geonamic
Resource type: Presentation
About this presentation
The PODS data model delivers a standardized, enterprise-grade schema purpose-built to support advanced AI/ML analytics for pipeline integrity management. By organizing inspection, weld, anomaly, dig list, repair, and asset data within a consistent, relational framework, PODS enables reliable feature engineering and high-quality training data for machine-learning workflows. This structured foundation supports sophisticated use cases such as multi-run anomaly matching, weld-based alignment, and AI-powered corrosion growth prediction. With PODS, operators can minimize data preparation efforts and focus on deploying scalable, predictive AI models that enable proactive, data-driven integrity decisions.
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Presented at the PODS Global Summit 2026, Doha, Qatar, February 12, 2026.