When we talk about IIoT, it is quite challenging to gather, identify, process and analyse any type of product, hood images manually at the EDGE location, near the Assembly Line, Tandem Press Line and Core Mining. But with the help of Amazon SageMaker it is quite east, cost effective and east to quick start, to train and deploy ML models on the top of IoT EDGE Devices. You can use RaspberryPi as an EDGE device to deploy your ML model, Amazon SageMaker to train and built your model and finally Greengrass to give your EDGE Devices most of the cloud capabilities to process your data at the EDGE location.
This solution is based on a ML model for image classification based on the CIFAR-10 dataset, which will be trained with Amazon SageMaker and then downloaded the trained model at AWS Greengrass to perform image classification at the EDGE device, which is connected with a camera to get images of products, car and hood. An EDGE device, here it is RaspberryPi running the Greengrass software to simply copy images to a directory which is regularly scanned and when an image is found it will be classified. Here Lambda function helps to load the model that has been deployed to Greengrass.