Category: AWS Greengrass

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 […]

In industrial automation, you will find dozens of protocols used by devices and PLC vendors: OPC-UA, Modbus TCP, serial, and more. In fact, often in just one facility, it is common to find two similar processes performing the same function with different equipment. AWS Greengrass reduces the operational overhead for businesses to change protocols as […]

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 […]