AWS Machine Learning Blog

Category: AWS IoT Greengrass

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 […]

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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 […]

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Building a trash sorter with AWS DeepLens

In this blog post, we show you how to build a prototype trash sorter using AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning in a fun, hands-on way. This prototype trash sorter project teaches you how to train image classification models with custom data. Image classification is a […]

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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 […]

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Enabling healthcare access from home: Electronic Caregiver’s AWS-powered virtual caregiver  

When Electronic Caregiver’s founder and CEO, Anthony Dohrmann, started the company a decade ago, he was reacting to a difficult situation faced by 100 million Americans and countless individuals globally: the challenge of managing health treatment for chronic diseases. “Patients are often confused about their care instructions and non-adherence with care plans and medications schedules […]

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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 […]

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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 […]

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AWS DeepLens Extensions: Build Your Own Project

AWS DeepLens provides a great opportunity to learn new technologies, such as deep learning and Internet of Things (IoT), as well as to build innovative systems that can solve real-world problems. The device and service comes with a set of predefined projects that make it easy to hit the ground running. It is designed as […]

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