Real-world adoption of deep neural networks (DNNs) in critical applications requires ensuring strong generalization beyond testing datasets. Unfortunately, the standard practice of measuring DNN performance on a finite set of test inputs cannot ensure DNN safety on inputs in the wild. In this talk, I will focus on how certified AI can be leveraged as a service to bridge this gap by building DNNs with strong generalization on an infinite set of unseen inputs. In the process, I will discuss some o…