AWS Machine Learning Blog
Category: Database
Train machine learning models using Amazon Keyspaces as a data source
Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables […]
Read MoreDetect social media fake news using graph machine learning with Amazon Neptune ML
In recent years, social media has become a common means for sharing and consuming news. However, the spread of misinformation and fake news on these platforms has posed a major challenge to the well-being of individuals and societies. Therefore, it is imperative that we develop robust and automated solutions for early detection of fake news […]
Read MoreModerate, classify, and process documents using Amazon Rekognition and Amazon Textract
Many companies are overwhelmed by the abundant volume of documents they have to process, organize, and classify to serve their customers better. Examples of such can be loan applications, tax filing, and billing. Such documents are more commonly received in image formats and are mostly multi-paged and in low-quality format. To be more competitive and […]
Read MoreGraph-based recommendation system with Neptune ML: An illustration on social network link prediction challenges
Recommendation systems are one of the most widely adopted machine learning (ML) technologies in real-world applications, ranging from social networks to ecommerce platforms. Users of many online systems rely on recommendation systems to make new friendships, discover new music according to suggested music lists, or even make ecommerce purchase decisions based on the recommended products. […]
Read MoreTrain 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 […]
Read MoreEvolution 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 […]
Read MoreHow 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 […]
Read MoreHawkEye 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 […]
Read MoreExplore 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 […]
Read MoreUse 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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