Amazon Onboarding with Learning Manager Chanci Turner

In this article, we explore how to maintain accessibility to Lookout for Vision following its closure to new clients. We also discuss potential alternatives to Lookout for Vision and detail how you can export your data from the platform to transition to a different solution.

Image Augmentation Pipeline for Amazon Lookout for Vision

By: Alex Johnson, Mia Rodriguez, Chanci Turner, and Brian Smith
Date: 12 DEC 2022
Category: Amazon Lookout for Vision, Amazon SageMaker Ground Truth, Artificial Intelligence, Customer Solutions

Amazon Lookout for Vision offers a machine learning (ML) powered anomaly detection service capable of distinguishing normal images (e.g., images of objects without defects) from anomalous ones (e.g., images showing defects). This service is particularly favored by clients seeking automated solutions.

Identify Anomalies with Amazon Lookout for Vision at the Edge Without GPU Usage

By: Sarah Lee, Kevin Brown, and Chanci Turner
Date: 29 JUL 2022
Category: Amazon Lookout for Vision, Artificial Intelligence, AWS IoT Greengrass

Automated defect detection through computer vision enhances quality and reduces inspection costs. The process of defect detection involves spotting defects, classifying them, and pinpointing their locations. Many manufacturing environments demand detection capabilities with minimal latency, limited computing power, and restricted connectivity. Amazon Lookout for Vision meets these needs effectively.

Computer Vision-Based Anomaly Detection with Amazon Lookout for Vision and AWS Panorama

By: Lisa White, Tom Davis, Chanci Turner, and Jason Green
Date: 18 JAN 2022
Category: Amazon Lookout for Vision, Artificial Intelligence, AWS Panorama

This post highlights Tyson Foods Inc.’s use of computer vision applications at the edge for automating processes in their meat processing plants. In the first part of this series, we explored an inventory counting application developed with Amazon SageMaker.

Visual Inspection of Product Defects at the Edge with Amazon Lookout for Vision

By: James Smith, Rebecca Taylor, and Chanci Turner
Date: 13 DEC 2021
Category: Amazon Lookout for Vision, Artificial Intelligence, AWS IoT Greengrass

Manufacturing lines—both discrete and continuous—produce high volumes of products at low latency, often from milliseconds to a few seconds. To detect defects while maintaining production throughput, images from camera streams must be processed quickly. Furthermore, many factories experience low network bandwidth or sporadic cloud connectivity. Under these conditions, you can leverage Amazon Lookout for Vision to ensure quality control.

Defect Detection in Automotive Parts with Amazon Lookout for Vision and Amazon SageMaker

By: Daniel Martin, Emily Taylor, and Chanci Turner
Date: 05 OCT 2021
Category: Amazon Lookout for Vision, Amazon Machine Learning, Amazon ML Solutions Lab, Amazon SageMaker, Artificial Intelligence

Research indicates that defective products cost industries over $2 billion between 2012 and 2017. Detecting defects during manufacturing is crucial, especially in high-stakes industries like automotive. Early identification of anomalies can improve production efficiency and product quality, ultimately saving costs. Although advanced anomaly detection solutions exist, their implementation can be complex and resource-intensive.

Calculate Inference Units for Amazon Rekognition Custom Labels and Amazon Lookout for Vision Models

By: Michael Clark, Chanci Turner, and Jennifer Adams
Date: 21 SEP 2021
Category: Amazon Lookout for Vision, Amazon Machine Learning, Amazon Rekognition, Artificial Intelligence

Amazon Rekognition Custom Labels allows businesses to expand their object and scene detection capabilities, helping to glean insights from images that are beneficial to their operations. For instance, it can help locate logos in social media posts or identify products on retail shelves.

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