AWS Machine Learning Blog

Category: Database

Train graph neural nets for millions of proteins on Amazon SageMaker and Amazon DocumentDB (with MongoDB compatibility)

There are over 180,000 unique proteins with 3D structures determined, with tens of thousands new structures resolved every year. This is only a small fraction of the 200 million known proteins with distinctive sequences. Recent deep learning algorithms such as AlphaFold can accurately predict 3D structures of proteins using their sequences, which help scale the […]

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Evolution of Cresta’s machine learning architecture: Migration to AWS and PyTorch

Cresta Intelligence, a California-based AI startup, makes businesses radically more productive by using Expertise AI to help sales and service teams unlock their full potential. Cresta is bringing together world-renowned AI thought-leaders, engineers, and investors to create a real-time coaching and management solution that transforms sales and increases service productivity, weeks after application deployment. Cresta […]

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How Careem is detecting identity fraud using graph-based deep learning and Amazon Neptune

This post was co-written with Kevin O’Brien, Senior Data Scientist in Careem’s Integrity Team. Dubai-based Careem became the Middle East’s first unicorn when it was acquired by Uber for $3.1 billion in 2019. A pioneer of the region’s ride-hailing economy, Careem is now expanding its services to include mass transportation, delivery, and payments as an […]

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HawkEye 360 predicts vessel risk using the Deep Graph Library and Amazon Neptune

This post is co-written by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) constellation, data, and analytics provider. Their signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, Automatic Identification System (AIS) beacons, emergency beacons, and more. The signals of interest library will […]

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Explore image analysis results from Amazon Rekognition and store your findings in Amazon DocumentDB

When we analyze images, we may want to incorporate other metadata related to the image. Examples include when and where the image was taken, who took the image, as well as what is featured in the image. One way to represent this metadata is to use a JSON format, which is well-suited for a document […]

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Use the AWS Cloud for observational life sciences studies

In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you […]

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Event-based fraud detection with direct customer calls using Amazon Connect

Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that […]

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Build a cognitive search and a health knowledge graph using AWS AI services

Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and […]

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Intelligent governance of document processing pipelines for regulated industries

Processing large documents like PDFs and static images is a cornerstone of today’s highly regulated industries. From healthcare information like doctor-patient visits and bills of health, to financial documents like loan applications, tax filings, research reports, and regulatory filings, these documents are integral to how these industries conduct business. The mechanisms by which these documents […]

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The following is the architecture diagram for integrating online ML inference in a telemedicine contact flow via Amazon Connect.

Applying voice classification in an Amazon Connect telemedicine contact flow

Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary ML classifier can classify healthy and […]

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