As enterprises and consumers increasingly adopt machine learning-enabled smart cameras, the expectations of these end users are becoming more sophisticated. In particular, smart camera users increasingly expect their deployed cameras to improve over time—for example, becoming more accurate and gaining new features. Traditionally, however, smart cameras that run machine learning at the edge have been difficult to upgrade. In this talk, we’ll explain how a cloud-native approach for running machine learning software at the edge enables smart camera developers to easily deploy improved models and new capabilities into existing, installed cameras. We’ll use application examples to illustrate the benefits of a cloud-native approach.