Using Amazon SageMaker inference pipelines with multi-model endpoints

Favorite Businesses are increasingly deploying multiple machine learning (ML) models to serve precise and accurate predictions to their consumers. Consider a media company that wants to provide recommendations to its subscribers. The company may want to employ different custom models for recommending different categories of products—such as movies, books, music,

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Shared by AWS Machine Learning October 22, 2020

Automatically detecting personal protective equipment on persons in images using Amazon Rekognition

Favorite Workplace safety hazards can exist in many different forms: sharp edges, falling objects, flying sparks, chemicals, noise, and a myriad of other potentially dangerous situations. Safety regulators such as Occupational Safety and Health Administration (OSHA) and European Commission often require that businesses protect their employees and customers from hazards

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Shared by AWS Machine Learning October 17, 2020

Processing auto insurance claims at scale using Amazon Rekognition Custom Labels and Amazon SageMaker Ground Truth

Favorite Computer vision uses machine learning (ML) to build applications that process images or videos. With Amazon Rekognition, you can use pre-trained computer vision models to identify objects, people, text, activities, or inappropriate content. Our customers have use cases that span every industry, including media, finance, manufacturing, sports, and technology.

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Shared by AWS Machine Learning October 16, 2020

Building a medical image search platform on AWS

Favorite Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% [1]. In additon, the report concludes that “burnout and satisfaction with work-life balance in US physicians worsened from

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Shared by AWS Machine Learning October 15, 2020

Amazon Personalize improvements reduce model training time by up to 40% and latency for generating recommendations by up to 30%

Favorite We’re excited to announce new efficiency improvements for Amazon Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables you to build

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Shared by AWS Machine Learning October 14, 2020