KubeCon + CloudNative North America 2021

Whether you are developing an IIoT application or an IoMT solution that requires closed-loop decisions, bringing machine intelligence closer to the edge offers significant advantages. Energy-efficient ML hardware for tensor operations is driving the move towards Edge ML and the emergence of a new domain of edge computing called AIoT or Artificial Intelligence of Things. This talk will propose a novel approach to build AIoT applications using Kubernetes. Using an illustrative reference architecture, the talk will explain emergent AIoT behaviors and architecture patterns. Such patterns can help in deciding what open source projects to choose for AIoT solutions. You will also see a demo simulating an industrial IoT setting where sensors measure conditions in real-time and feed them to an edge device. The device uses a TPU accelerator to apply logistic regression to predict future equipment breakdown. You will see how ML pipelines measure drift and re-train and re-deploy the model.