Explore the intricacies of implementing trusted AI through machine learning payload logging on Kubernetes in this conference talk by Tommy Li and Andrew Butler from IBM. Delve into the challenges of production model serving, examining KNative and KF Serving configurations, supported frameworks, and storage subsystems. Investigate the inference service control plane and KFServing deployment views with practical examples. Address the critical question of prediction trustworthiness in production ML architectures, focusing on payload logging. Discover the Linux Foundation's approach to the Trusted AI lifecycle through open source initiatives. Examine the importance of AI explainability, bias detection in criminal justice systems, and adversarial robustness. Learn about AI Explainability 360 and AI Fairness 360 tools, and explore LFAI Trusted AI projects integrated with Kubeflow Serving. Conclude with a demonstration that ties together the concepts presented.