The Internet of Things on AWS – Official Blog

Category: AWS IoT Greengrass

Building Edge Solutions on OpenWrt with AWS IoT Greengrass

Hardware and operating systems for edge devices are built and delivered to customers in a wide range of form factors, such as consumer gateways, industrial controllers, and in-vehicle infotainment systems. Original design manufacturers (ODMs) and original equipment manufacturers (OEMs) building commercial off-the-shelf products decide which hardware and software combinations to use. System integrators (SIs) developing […]

How to Install a Face Recognition Model at the Edge with AWS IoT Greengrass

How to Install a Face Recognition Model at the Edge with AWS IoT Greengrass

Editor Note: This post is co-authored by Gong Xinyue, one of Global Accounts Solution Architects. You might already know how to use AWS IoT Core and AWS IoT Greengrass for remote device communication and control. With AWS IoT Greengrass Machine Learning (ML) Inference, you can run machine learning models on your local devices without any […]

Deploy AWS IoT Greengrass as a Snap to Your Edge Devices

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 […]

Using AWS IoT Services for Asset Condition Monitoring

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 […]

Machine Learning at the Edge: Using and Retraining Image Classification Models with AWS IoT Greengrass (Part 2)

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 […]

Machine Learning at the Edge: Using and Retraining Image Classification Models with AWS IoT Greengrass (Part 1)

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 now enables simplified deployments, enhanced security, and greater flexibility

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 […]

Using AWS IoT for Predictive Maintenance

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 […]

Using Chainer Neural Network Framework with AWS Greengrass ML Inference

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, […]