AWS Machine Learning Blog
Category: Internet of Things
Abode uses Amazon Rekognition Streaming Video Events to provide real-time notifications to their smart home customers
Abode Systems (Abode) offers homeowners a comprehensive suite of do-it-yourself home security solutions that can be set up in minutes and enables homeowners to keep their family and property safe. Since the company’s launch in 2015, in-camera motion detection sensors have played an essential part in Abode’s solution, enabling customers to receive notifications and monitor […]
Anomaly detection with Amazon SageMaker Edge Manager using AWS IoT Greengrass V2
Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial […]
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 […]
Smart city traffic anomaly detection using Amazon Lookout for Metrics and Amazon Kinesis Data Analytics Studio
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Cities across the world are transforming their public services infrastructure with the mission of enhancing the quality of life of its residents. Roads and traffic management systems […]
Protecting people from hazardous areas through virtual boundaries with Computer Vision
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. As companies welcome more autonomous robots and other heavy […]
Building a lawn monitor and weed detection solution with AWS machine learning and IoT services
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. August 30, 2023: Amazon Kinesis Data Analytics has been […]
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 […]