Category: AWS IoT Greengrass

Industrial IoT (IIoT) bridges the gap between industrial equipment and automation networks (usually called OT, Operations Technology) and Information Technology (IT). In IT, use of new technologies such as machine learning, cloud, mobile, and edge computing are becoming commonplace. IIoT brings machines, cloud computing, analytics, and people together to improve performance, productivity, and efficiency of […]

Post by Angela Wang and Tanner McRae, Engineers on the AWS Solutions Architecture R&D and Innovation team This post is the second in a series on how to build and deploy a custom object detection model to the edge using Amazon SageMaker and AWS IoT Greengrass. In part 1 of this series, we walked through the training […]

Post by Angela Wang and Tanner McRae, Engineers on the AWS Solutions Architecture R&D and Innovation team Running computer vision algorithms at the edge unlocks many industry use cases that has low or limited internet connectivity. Combining services from AWS in the Machine Learning (ML) and Internet of Things (IoT) space, training a custom computer vision model and running […]

This post demonstrates how to build a continuous integration and continuous deployment (CI/CD) pipeline for AWS IoT Greengrass projects. Readers should be familiar with AWS IoT Greengrass. This post focuses on streamlining the development process for AWS IoT Greengrass projects rather than explaining the technology itself. If you are not familiar with AWS IoT Greengrass, […]

Co-authored by Aniruddh Chitre, AWS Solutions Architect This post demonstrates how AWS IoT Greengrass can be integrated with a Trusted Platform Module (TPM) to provide hardware-based endpoint device security. This integration ensures the private key used to establish device identity can be securely stored in tamper-proof hardware devices to prevent it from being taken out […]

Gaining access to sensor data or telemetry of industrial machines is a key requirement for implementing high-value use cases around smart manufacturing or Industry 4.0. For instance, predictive maintenance or automated quality control is not possible without having such data at a high temporal resolution. Given the heterogeneous environment of modern industrial production lines with […]

Industrial customers are increasingly using the AWS Cloud to meet their targets for predictive quality, predictive maintenance, and asset condition monitoring. For more examples, see the Top Use Cases for Industrial IoT Applications ebook. The first of these, predictive quality, is often strongly correlated with the level of safety in an operating environment. In this […]

AWS IoT Greengrass extends AWS managed services to edge device systems, providing security, message routing, and local state processing. It also provides a central hub for connectivity with other AWS services using AWS Lambda functions. Some edge systems with multiple devices produce high-frequency updates, such as device shadow updates. It can be difficult to determine […]

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 […]

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 […]