Edge computing is redefining how we deploy and manage machine learning (ML) models. Instead of sending every data point to the cloud, DevOps at the edge brings model inference directly onto IoT devices — enabling low-latency predictions, offline operation, and improved privacy. 

However, pushing AI to a fleet of heterogeneous, resource-constrained devices introduces new complexities. This article explores how DevOps practices can be applied to edge ML deployments on IoT hardware. We will discuss key tools, walk through a hands-on example of deploying a model to an IoT device with CI/CD, and address common challenges (model versioning, limited compute, intermittent connectivity) along the way.