AWS IoT VS AWS Greengrass

In this blog post series, we will look at a simple example of modeling an IoT device process as a workflow, using primarily AWS IoT and AWS Step Functions. Our example is a system where, when a device comes online, you need to get external settings based on the profile of the user the device belongs to and push that configuration to the device. The system that holds the external settings is often a third party... - Source: dev.to / about 1 year ago

Iot - MQTT broker to send messages to the Raspberry Pi. - Source: dev.to / over 2 years ago

" Amazon Web Services offers a broad set of global cloud-based products including compute, storage, databases, analytics, networking, mobile, developer tools, management tools, IoT, security and enterprise applications. These services help organizations move faster, lower IT costs, and scale. AWS is trusted by the largest enterprises and the hottest start-ups to power a wide variety of workloads including: web and... Source: over 2 years ago

AWS IoT Core - message broker between all devices and AWS. - Source: dev.to / over 2 years ago

If you have to ask, then you should be using AWS by default. They have plenty of IoT services for you to fiddle around with and get started. Source: over 2 years ago

In this blog post series, we will demonstrate how to use AWS IoT Greengrass and Hashicorp Nomad to seamlessly interface with multiple interconnected devices and orchestrate service deployments on them. Greengrass will allow us to view the cluster as a single "Thing" from the cloud perspective, while Nomad will serve as the primary cluster orchestration tool. - Source: dev.to / about 1 year ago

AWS IoT Greengrass allows one to manage their IOT Edge devices, download ML models locally, so that inference can then be also be done locally. - Source: dev.to / almost 2 years ago

To assist in deployment and management of workloads in your fleet, it's worth taking advantage of a fleet or device management tool such as AWS GreenGrass, Formant or Rocos. - Source: dev.to / over 2 years ago

In some cases, such as with edge devices, inferencing needs to occur even when there is limited or no connectivity to the cloud. Mining fields are an example of this type of use case. AWS IoT Greengrass enables ML inference locally using models that are created, trained, and optimized in the cloud using Amazon SageMaker, AWS Deep Learning AMI, or AWS Deep Learning Containers, and deployed on the edge devices. - Source: dev.to / over 2 years ago

Take a look at Greengrass https://aws.amazon.com/greengrass/ Enables OTA updates and fleet management. Source: almost 3 years ago