Category: AWS IoT Greengrass

Canonical and AWS have collaborated to offer AWS IoT Greengrass as snap, a containerized software package that can run on a variety of Linux distributions. The combination of AWS IoT Greengrass and Ubuntu Core provides IoT developers with a fast path from development to production for their secure device. The AWS IoT Greengrass snap, available […]

In this blog post, I will show you how you can use AWS CloudFormation to set up AWS IoT Greengrass quickly and in a repeatable manner. I will discuss the AWS IoT Greengrass resource types and how they are related to a Greengrass group deployment. I will also demonstrate a deployment that uses an AWS […]

The Industrial Internet of Things (IIoT) presents an unparalleled opportunity for every industry to address core business challenges, such as reducing downtime, improving safety, increasing system output, reducing operating costs, and creating innovative services and business models. In this blog post, I will show how you can use AWS IoT services to build an asset […]

In part 1 of this blog post, we created an image classification model for a recycling facility’s sorter to identify four beverage containers. We deployed it to our AWS IoT Greengrass Core device using the new AWS IoT Greengrass Image Classification connector. AWS IoT Greengrass connectors, announced at this year’s re:Invent, make it possible for […]

With the introduction of the AWS IoT Greengrass Image Classification connector at this year’s re:Invent, it has become easier than ever to use image classification at the edge through AWS IoT Greengrass. Because it is software that lives on a local device, AWS IoT Greengrass makes it possible to analyze data closer to the source […]

AWS IoT Greengrass allows you to bring local compute, messaging, data caching, sync, and ML inference capabilities to edge devices. Our newest release introduces features that simplify the deployment of Lambda functions to Greengrass, provide more flexibility so you can deploy Greengrass to new environments, and add easy-to-use security capabilities. Starting today, you can use […]

The interest in machine learning for industrial and manufacturing use cases on the edge is growing. Manufacturers need to know when a machine is about to fail so they can better plan for maintenance. For example, as a manufacturer, you might have a machine that is sensitive to various temperature, velocity, or pressure changes. When […]

Starting today, Greengrass ML inference includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson TX2, and Raspberry Pi. So, you don’t have to build and configure the ML framework for your devices from scratch. With this launch, we now provide pre-built packages for three popular machine learning frameworks including TensorFlow, […]

Shane Baldacchino is a Solutions Architect at Amazon Web Services. Customers have been asking how compute at the edge can be coupled with AWS services to advance maintenance maturity in their organization. In this blog post, we will examine maintenance maturity through the lens of something very common: elevators. We will show how you can […]