Reliability Meets AI

Reliability Meets AI: An Introductory Series on Models, Maintenance, and Trust

This four-part introductory webinar series provides a practical on-ramp to applying AI in reliability engineering without assuming a data science background. It starts by translating common reliability questions (failure risk, time-to-failure, early warning, and maintenance decisions) into core ML/AI problem types and highlighting what makes reliability data analysis unique. The series then surveys key analysis techniques, including classification and regression, degradation/RUL prediction, anomaly and change detection, and approaches for repairable systems, to frame when to use classical reliability methods, when ML adds value, and how to combine them responsibly. Next, it explores several AI applications across the reliability lifecycle, from predictive maintenance and test planning to text/vision analytics, knowledge graphs, and LLM-based copilots for reliability workflows. Finally, it focuses on the “reliability of AI,” introducing a reliability engineering mindset for deployed models: failure modes, robustness to shift, calibration and uncertainty, monitoring, and safe change control. Participants leave with a clear mental map of the reliability-AI interaction space, a method-selection perspective, and actionable ideas for low-risk pilots and trustworthy deployment.