AWS Big Data Blog
Category: Amazon Machine Learning
How Encored Technologies built serverless event-driven data pipelines with AWS
This post is a guest post co-written with SeonJeong Lee, JaeRyun Yim, and HyeonSeok Yang from Encored Technologies. Encored Technologies (Encored) is an energy IT company in Korea that helps their customers generate higher revenue and reduce operational costs in renewable energy industries by providing various AI-based solutions. Encored develops machine learning (ML) applications predicting […]
Automate discovery of data relationships using ML and Amazon Neptune graph technology
Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to […]
How SikSin improved customer engagement with AWS Data Lab and Amazon Personalize
This post is co-written with Byungjun Choi and Sangha Yang from SikSin. SikSin is a technology platform connecting customers with restaurant partners serving their multiple needs. Customers use the SikSin platform to search and discover restaurants, read and write reviews, and view photos. From the restaurateurs’ perspective, SikSin enables restaurant partners to engage and acquire […]
Near-real-time fraud detection using Amazon Redshift Streaming Ingestion with Amazon Kinesis Data Streams and Amazon Redshift ML
The importance of data warehouses and analytics performed on data warehouse platforms has been increasing steadily over the years, with many businesses coming to rely on these systems as mission-critical for both short-term operational decision-making and long-term strategic planning. Traditionally, data warehouses are refreshed in batch cycles, for example, monthly, weekly, or daily, so that […]
Data: The genesis for modern invention
It only takes one groundbreaking invention—one iconic idea that solves a widespread pain point for customers—to create or transform an industry forever. From the invention of the telegraph, to the discovery of GPS, to the earliest cloud computing services, history is filled with examples of these “eureka” moments that continue to have long-lasting impacts on […]
How Fresenius Medical Care aims to save dialysis patient lives using real-time predictive analytics on AWS
This post is co-written by Kanti Singh, Director of Data & Analytics at Fresenius Medical Care. Fresenius Medical Care is the world’s leading provider of kidney care products and services, and operates more than 2,600 dialysis centers in the US alone. The company provides comprehensive solutions for people living with chronic kidney disease and related […]
Build machine learning-powered business intelligence analyses using Amazon QuickSight
Imagine you can see the future—to know how many customers will order your product months ahead of time so you can make adequate provisions, or to know how many of your employees will leave your organization several months in advance so you can take preemptive actions to encourage staff retention. For an organization that sees […]
Decreasing Game Churn: How Upopa used ironSource Atom and Amazon ML to Engage Users
This is a guest post by Tom Talpir, Software Developer at ironSource. ironSource is as an Advanced AWS Partner Network (APN) Technology Partner and an AWS Big Data Competency Partner. Ever wondered what it takes to keep a user from leaving your game or application after all the hard work you put in? Wouldn’t it be great […]
Powering Amazon Redshift Analytics with Apache Spark and Amazon Machine Learning
Air travel can be stressful due to the many factors that are simply out of airline passengers’ control. As passengers, we want to minimize this stress as much as we can. We can do this by using past data to make predictions about how likely a flight will be delayed based on the time of […]
Readmission Prediction Through Patient Risk Stratification Using Amazon Machine Learning
In this post, I show how to apply advanced analytics concepts like pattern analysis and machine learning to do risk stratification for patient cohorts.