AWS Database Blog
Category: Amazon SageMaker
Up your game: Increase player retention with ML-powered matchmaking using Amazon Aurora ML and Amazon SageMaker
Organizations are looking for ways to better leverage their data to improve their business operations. With Amazon Aurora, Aurora Machine Learning, and Amazon SageMaker, you can train machine learning (ML) services quickly and directly integrate the ML model with your existing Aurora data to better serve your customers. In this post, we demonstrate how a […]
Adding real-time machine learning predictions to Amazon Aurora: Part 1
Businesses today want to enhance the data stored in their relational databases and incorporate up-to-the-minute predictions from machine learning (ML) models. However, most ML processing is done offline in separate systems, resulting in delays in receiving ML inferences for use in applications. AWS wants to make it efficient to incorporate real-time model inferences in your […]
Use Amazon ElastiCache for Redis as a near-real-time feature store
Customers often use Amazon ElastiCache for real-time transactional and analytical use cases. It provides high throughout and low latencies, while meeting a variety of business needs. Because it uses in-memory data structures, typical use cases include database and session caching, as well as leaderboards, gaming and financial trading platforms, social media, and sharing economy apps. […]
Accelerating your application modernization with Amazon Aurora Machine Learning
Organizations that store and process data in relational databases are making the shift to the cloud. As part of this shift, they often wish to modernize their application architectures and add new cloud-based capabilities. Chief among these are machine learning (ML)-based predictions such as product recommendations and fraud detection. The rich customer data available in […]
Getting started with Amazon DocumentDB (with MongoDB compatibility); Part 4 – using Amazon SageMaker notebooks
In this post, we demonstrate how to use Amazon SageMaker notebooks to connect to Amazon DocumentDB for a simple, powerful, and flexible development experience. We walk through the steps using the AWS Management Console, but also include an AWS CloudFormation template to add an Amazon SageMaker notebook to your existing Amazon DocumentDB environment.
Building and querying the AWS COVID-19 knowledge graph
This blog post details how to recreate the AWS COVID-19 knowledge graph (CKG) using AWS CloudFormation and Amazon Neptune, and query the graph using Jupyter notebooks hosted on Amazon SageMaker in your AWS account. The CKG aids in the exploration and analysis of the COVID-19 Open Research Dataset (CORD-19), hosted in the AWS COVID-19 data […]