AWS Machine Learning Blog

Category: AWS IoT Greengrass

MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, […]

Identify the location of anomalies using Amazon Lookout for Vision at the edge without using a GPU

Automated defect detection using computer vision helps improve quality and lower the cost of inspection. Defect detection involves identifying the presence of a defect, classifying types of defects, and identifying where the defects are located. Many manufacturing processes require detection at a low latency, with limited compute resources, and with limited connectivity. Amazon Lookout for […]

Amazon Lookout for Vision now supports visual inspection of product defects at the edge

Discrete and continuous manufacturing lines generate a high volume of products at low latency, ranging from milliseconds to a few seconds. To identify defects at the same throughput of production, camera streams of images must be processed at low latency. Additionally, factories may have low network bandwidth or intermittent cloud connectivity. In such scenarios, you […]

Build machine learning at the edge applications using Amazon SageMaker Edge Manager and AWS IoT Greengrass V2

Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the cloud. Although there are numerous ways to train ML models for countless applications, effectively optimizing and deploying these models for IoT devices can present many […]

Run ML inference on AWS Snowball Edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these devices, deploying and managing ML models […]

Building a trash sorter with AWS DeepLens

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. In this blog post, we show you how to […]

Parallelizing across multiple CPU/GPUs to speed up deep learning inference at the edge

AWS customers often choose to run machine learning (ML) inferences at the edge to minimize latency. In many of these situations, ML predictions must be run on a large number of inputs independently.  For example, running an object detection model on each frame of a video. In these cases, parallelizing ML inferences across all available CPU/GPUs […]

Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 2

In Part 1 of this blog post, we demonstrated how to train and deploy neural networks to automatically segment brain tissue from an MRI scan in a simple, streamlined way using Amazon SageMaker. We used Apache MXNet to train a convolutional neural network (CNN) on Amazon SageMaker using the Bring Your Own Script paradigm. We […]

Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 1

Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level. In Part 1 of this blog post, we demonstrate how to train […]

AWS DeepLens Extensions: Build Your Own Project

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. AWS DeepLens provides a great opportunity to learn new […]