In the final installment of "The Vision of DevOps for Devices" series, the article explores the operationalization of Artificial Intelligence (AI) and Machine Learning (ML) at the edge, focusing on the complexities of delivering AI models to edge devices and how Esper's platform is uniquely equipped to address these challenges. It highlights the benefits of AI at the edge, such as enhanced privacy, improved efficiency, and scalability, while emphasizing the difficulties in deploying AI models in a consistent and scalable manner. The text discusses solutions like CI/CD pipelines, containerization, and testing to streamline the deployment and updating of AI models on edge devices, ensuring they run locally to overcome latency and bandwidth issues. Esper's platform offers tools like Blueprints, Pipelines, and an app and content library to facilitate effective device management and AI model deployment, allowing businesses to focus on improving their AI capabilities and strategic technology choices. The article concludes by mentioning that Esper is continuously enhancing its platform to incorporate more AI functionalities, promising future advancements for its users.