  In recent years we have witnessed a boom in Internet of Things (IoT) device
deployments, which has resulted in big data and demand for low-latency
communication. This shift in the demand for infrastructure is also enabling
real-time decision making using artificial intelligence for IoT applications.
Artificial Intelligence of Things (AIoT) is the combination of Artificial
Intelligence (AI) technologies and the IoT infrastructure to provide robust and
efficient operations and decision making. Edge computing is emerging to enable
AIoT applications. Edge computing enables generating insights and making
decisions at or near the data source, reducing the amount of data sent to the
cloud or a central repository. In this paper, we propose a framework for
facilitating machine learning at the edge for AIoT applications, to enable
continuous delivery, deployment, and monitoring of machine learning models at
the edge (Edge MLOps). The contribution is an architecture that includes
services, tools, and methods for delivering fleet analytics at scale. We
present a preliminary validation of the framework by performing experiments
with IoT devices on a university campus's rooms. For the machine learning
experiments, we forecast multivariate time series for predicting air quality in
the respective rooms by using the models deployed in respective edge devices.
By these experiments, we validate the proposed fleet analytics framework for
efficiency and robustness.
