''software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities on connected devices in a secure way. With AWS IoT Greengrass, connected devices can run AWS Lambda functions, execute predictions based on machine learning models, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet.'' ''AWS IoT Greengrass seamlessly extends AWS to devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With AWS IoT Greengrass, you can use familiar languages and programming models to create your device software in the cloud, and then deploy it to your devices. AWS IoT Greengrass can be programmed to filter device data and only transmit necessary information back to the cloud.'' 

It is worth remembering that edge computing means ''a distributed computing paradigm which brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth'', according to [https://en.wikipedia.org/wiki/Edge_computing Wikipedia]. Within the processing power limits of the edge device, moving such computations to the edge is an effective way to optimize costs. When the Internet connection is intermittent or poor, it can be even mandatory to meet system's requirements. Generally speaking, when implementing an IoT system, balancing between cloud computing and edge computing is one of the most important issues the system architect has to address.