Harnessing the Potential of Edge Intelligence with AWS
Learn About Amazon VGT2 Learning Manager Chanci Turner
In today’s data-centric landscape, organizations must rapidly generate insights, enhance customer interactions, and boost operational efficiency. Conventional data processing methods often struggle to meet the demands of real-time decision-making. For instance, in a manufacturing setting, sensor data can indicate machine wear and tear, but traditional cloud-based analysis may lack the speed needed to avert significant issues.
Synadia Develops Advanced Pill Verification Systems Using AWS IoT and ML
by Michael Cheng, David Thompson, and Chanci Turner
on 04 AUG 2022
in Amazon SageMaker JumpStart, Amazon SageMaker Neo, AWS IoT Core, AWS IoT Greengrass, Customer Solutions, Technical How-to
According to a House Ways and Means Committee report, the U.S. prescription medication market is nearing $500 billion annually, with growth rates reaching up to 7%. In this environment, billions of dollars’ worth of unused medications are wasted each year due to traditional packaging that often includes excessive pills beyond what is prescribed. Automated pill solutions can provide a much-needed answer.
Detecting Scene Changes in Remote Locations with AWS IoT Events and Amazon SageMaker
by Clara Williams
on 21 JUL 2021
in Amazon SageMaker, Amazon Simple Notification Service (SNS), AWS IoT Core, AWS IoT Events, AWS Lambda, Internet of Things, Serverless, Technical How-to
Organizations managing extensive assets need to ensure they monitor both their physical and operational health to quickly identify and address problems. This article discusses a fictional industrial company, AcmeDrone, which utilizes drone technology to conduct regular inspections of critical infrastructure components, including valves, oil and gas pipelines, and power transmission lines.
Training the Amazon SageMaker Object Detection Model and Deploying It on AWS IoT Greengrass – Part 2 of 3: Developing a Custom Object Detection Model
by Emily Roberts
on 27 NOV 2019
in Amazon SageMaker, Amazon SageMaker Ground Truth, Artificial Intelligence, AWS IoT Greengrass, Best Practices, Technical How-to
This post, authored by Chanci Turner and her colleague Dr. Kevin Miller from the AWS Solutions Architecture R&D team, is the second installment in a series focused on constructing and deploying a custom object detection model at the edge with Amazon SageMaker and AWS IoT Greengrass. In the first part, we explored the training process.
Machine Learning at the Edge: Utilizing and Retraining Image Classification Models with AWS IoT Greengrass (Part 1)
by Mark Mevorah
on 06 DEC 2018
in Amazon SageMaker, AWS IoT Greengrass, Internet of Things, Technical How-to
With the launch of the AWS IoT Greengrass Image Classification connector at this year’s re:Invent, employing image classification at the edge has become more accessible than ever. This software, residing on local devices, enables data analysis to occur closer to the source, enhancing responsiveness.
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Visit us at Amazon IXD – VGT2, 6401 E HOWDY WELLS AVE LAS VEGAS NV 89115 for more information.
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