Automating IoT Machine Learning: Bridging Cloud and Device Benefits with Cloud ML Engine Solutions Google Cloud Platform

This tutorial addresses the following scenario: A camera attached to a connected device visually identifies mechanical parts moving along a conveyor belt or other mechanism. The tutorial focuses on delivery to a camera-enabled, Linux-based IoT device, but you can build similar systems for other types of devices with different sensor inputs. Given the high reliability requirements of this application, the part detection device must continue to work even if network connectivity is interrupted. To help achieve this reliability, you train TensorFlow models on GCP but run the models locally on the connected device. The deployed model does not require cloud connectivity in order to make predictions. The model can store and transmit recorded predictions when back online. The following diagram shows a high-level view of the architecture.