AWS Partner Network (APN) Blog

Category: Amazon Machine Learning

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How Onica Leverages AWS AI, ML, and IoT Services to Combat the Pandemic

Many organizations have started applying machine learning and artificial intelligence expertise to scale customer communications and accelerate research during the COVID-19 pandemic. Onica has been actively involved in these efforts, leveraging AWS technologies to help decision makers navigate this pandemic. In this post, dive into the technical details of two COVID-19-related solutions Onica has produced and learn about their results and impact.

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How Pr3vent Uses Machine Learning on AWS to Combat Preventable Vision Loss in Infants

Scaling doctors’ expertise through artificial intelligence (AI) and machine learning (ML) provides an affordable and accurate solution, giving millions of infants equal access to eye screening. Learn how Pr3vent, a medical AI company founded by ophthalmologists, teamed up with AWS Machine Learning Competency Partner Provectus to develop an advanced disease screening solution powered by deep learning that detects pathology and signs of possible abnormalities in the retinas of newborns.

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How to Build and Deploy Amazon SageMaker Models in Dataiku Collaboratively

Organizations often need business analysts and citizen data scientists to work with data scientists to create machine learning (ML) models, but they struggle to provide a common ground for collaboration. Newly enriched Dataiku Data Science Studio (DSS) and Amazon SageMaker capabilities answer this need, empowering a broader set of users by leveraging the managed infrastructure of Amazon SageMaker and combining it with Dataiku’s visual interface to develop models at scale.

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Intelligent Video Analytics and Effective Remote Learning on Campus Private 4G/5G Networks

Edge computing is a new paradigm in which the resources of a small data center are placed at the edge of the internet, in close proximity to mobile devices, sensors, and end users. Learn about the Physical Distancing Video Analytics Solution (VAS) on campus private 4G/5G networks that was developed utilizing AWS edge services in partnership with Carnegie Mellon University’s Open Edge Computing Initiative, Megh Computing’s Video Analytics Solution, and Federated Wireless Private Network Connectivity as a Service.

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How AWS Machine Learning Services Increase Medical Coding Accuracy and Efficiency

Medical coding helps providers maintain patient records and obtain reimbursement for services. Unfortunately, the process is complicated, time-consuming, and prone to error. Learn how ClearScale developed a solution that increases the efficiency and accuracy of the coding process. Powered by AWS Machine Learning, the application translates recorded medical appointment notes, and uses the information to generate more accurate medical codes.

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How Provectus and GoCheck Kids Built ML Infrastructure for Improved Usability During Vision Screening

For businesses like GoCheck Kids, machine learning infrastructure is vital. The company has developed a next-generation, ML-driven pediatric vision screening platform that enables healthcare practitioners to screen for vision risks in children in a fast and easy way by utilizing GoCheck Kids’ smartphone app. Learn how GoCheck Kids teamed up with Provectus to build a secure, auditable, and reproducible ML infrastructure on AWS to ensure its solution is powered by highly accurate image classification model.

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How to Deploy AI Inference on the Edge with the LG AIoT Board and AWS IoT Greengrass

The growth of AI in a wide range of applications demands more purpose-built processors to provide scalable levels of performance, flexibility, and efficiency. The LG AIoT board helps customers accelerate their computer vision and machine learning journey using AWS. Learn how to build a simple AI-enabled application with AWS IoT Greengrass that takes advantage of the hardware AI acceleration on the LG AIoT board. AWS IoT Greengrass extends AWS on your device and offers the cloud programming model and tools at the edge.

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Interactive Scientific Visualization on AWS with NVIDIA IndeX SDK

Scientific visualization is critical to understand complex phenomena modeled using high performance computing simulations. However, it has been challenging to do this effectively due to the inability to visualize, explore, and analyze large volumes of data and lack of collaborative workflow solutions. NVIDIA IndeX on AWS addresses each of these problems by providing a scientific visualization solution for massive datasets, thus opening the doors for discovery.

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Amazon Fraud Detector Can Accelerate How AI is Embedded in Your Business

Online fraud is estimated to be costing businesses billions of dollars a year. As Fraudsters evolve new behaviors to get around preventive measures, businesses need a strategy that enables them to be responsive to new problems as they emerge. Learn how Inawisdom uses Amazon Fraud Detector to accelerate how AI can be embedded in a company’s strategy. What makes machine learning more flexible is its focus on identifying general patterns by looking at lots of examples.

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Using Fewer Resources to Run Deep Learning Inference on Intel FPGA Edge Devices

Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.

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