AWS Announces General Availability of Amazon Lookout for Vision
Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced the general availability of Amazon Lookout for Vision, a new service that analyzes images using computer vision and sophisticated machine learning capabilities to spot product or process defects and anomalies in manufactured products. By employing a machine learning technique called “few-shot learning,” Amazon Lookout for Vision is able to train a model for a customer using as few as 30 baseline images. Customers can get started quickly using Amazon Lookout for Vision to detect manufacturing and production defects (e.g. cracks, dents, incorrect color, irregular shape, etc.) in their products and prevent those costly errors from progressing down the operational line and from ever reaching customers. Together with Amazon Lookout for Equipment, Amazon Monitron, and AWS Panorama, Amazon Lookout for Vision provides industrial and manufacturing customers with the most comprehensive suite of cloud-to-edge industrial machine learning services available. With Amazon Lookout for Vision, there is no up-front commitment or minimum fee, and customers pay by the hour for their actual usage to train the model and detect anomalies or defects using the service. To get started with Amazon Lookout for Vision, visit https://aws.amazon.com/lookout-for-vision/
In today’s manufacturing industry, production line shutdowns due to missed defects or quality inconsistencies can result in millions of dollars of cost overruns and lost revenue every year. To avoid these expensive issues, industrial companies must maintain constant diligence to ensure quality control. Quality assurance in industrial processes typically requires human inspection, which can be tedious and inconsistent at best, or at worst, infeasible. Computer vision brings the speed and accuracy needed to identify defects consistently; however, implementing traditional computer vision solutions can be complex. Building computer vision models from scratch requires large amounts of carefully labeled images for each element of the manufacturing process. Then, teams of data scientists need to build, train, deploy, monitor, and fine tune computer vision models to analyze each individual phase of the product inspection process. Even small changes in the manufacturing process (e.g. replacing an out of stock component with an equivalent alternative, updates to the product specifications, or a change in lighting) means having to retrain and redeploy the individual model and perhaps other models downstream in the production process, which is tedious, complex, and time consuming. Because of these barriers, computer vision-powered visual anomaly systems remain out of reach for the vast majority of companies.
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