AWS Database Blog

Category: Artificial Intelligence

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 […]

How Amazon DevOps Guru for RDS helps NRI Digital with database performance monitoring

This guest post is co-authored by Ryota Shima, Application Architect, and Kazuki Matsumura, Lead Architect at NRI Digital. NRI Digital has a wide variety of systems in production, both on-premises and cloud-based. Among them, many systems are built on AWS, and Amazon Aurora and Amazon Relational Database Service (Amazon RDS) are often used as the […]

Amazon DevOps Guru for RDS under the hood

Amazon DevOps Guru for RDS is a new capability for Amazon DevOps Guru that helps developers using Amazon Aurora database instances detect, diagnose, and resolve database performance issues fast and at scale. DevOps Guru for RDS uses machine learning (ML) to automatically identify and analyze a wide range of performance-related database issues, such as over-utilization […]

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. […]

Supercharge your knowledge graph using Amazon Neptune, Amazon Comprehend, and Amazon Lex

Knowledge graph applications are one of the most popular graph use cases being built on Amazon Neptune today. Knowledge graphs consolidate and integrate an organization’s information into a single location by relating data stored from structured systems (e.g., e-commerce, sales records, CRM systems) and unstructured systems (e.g., text documents, email, news articles) together in a […]

Diagram shows a Lambda function consuming the DynamoDB streams and interacting with Amazon Comprehend and with Kinesis Firehose.

Integrate your Amazon DynamoDB table with machine learning for sentiment analysis

Amazon DynamoDB is a non-relational database that delivers reliable performance at any scale. It’s a fully managed, multi-Region, multi-active database that provides consistent single-digit millisecond latency and offers built-in security, backup and restore, and in-memory caching. DynamoDB offers a serverless and event-driven architecture, which enables you to use other AWS services to extend DynamoDB capability. […]

As we discussed earlier, the class column differentiates between bots and humans: class=1 is bot acceleration, class=0 is human acceleration.

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 […]

The following diagram is a Neptune Workbench visualization of the relationship between a document, a corporate acquisition event, and the organizations (with their roles) involved in that event.

Building a knowledge graph in Amazon Neptune using Amazon Comprehend Events

On 28-Oct-22, the AWS CloudFormation template and Jupyter notebook linked in this post were updated to 1/ add openCypher queries along with the existing Gremlin and SPARQL queries, 2/ updated to use Sagemaker newer Amazon Linux 2 instances, 3/ fixed a bug in the RDF generation code that improperly labeled a property as an RDF […]

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.