The Internet of Things on AWS – Official Blog
Tag: machine learning at the edge
Deploying and benchmarking YOLOv8 on GPU-based edge devices using AWS IoT Greengrass
Introduction Customers in manufacturing, logistics, and energy sectors often have stringent requirements for needing to run machine learning (ML) models at the edge. Some of these requirements include low-latency processing, poor or no connectivity to the internet, and data security. For these customers, running ML processes at the edge offers many advantages over running them […]
Training the Amazon SageMaker object detection model and running it on AWS IoT Greengrass – Part 3 of 3: Deploying to the edge
Post by Angela Wang and Tanner McRae, Senior Engineers on the AWS Solutions Architecture R&D and Innovation team This post is the third 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 the previous 2 parts of the series, we walked […]
Detect Cryptocurrency Mining Threats on Edge Devices using AWS IoT
Introduction Machine learning (ML) at the edge requires powerful edge requires powerful edge devices with a unique set of requirements. The availability, safety, and security requirements for the edge differ from cloud since they are located at the customer site, outside the data center, and interface directly with operational technology (OT) and the internet. Since […]
Training the Amazon SageMaker object detection model and running it on AWS IoT Greengrass – Part 1 of 3: Preparing training data
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 […]