AWS Database Blog

Category: Amazon SageMaker

Predictive Analytics with Time-series Machine Learning on Amazon Timestream

Capacity planning for large applications can be difficult due to constantly changing requirements and the dynamic nature of modern infrastructures. Traditional reactive approaches, for instance, relying on static thresholds for some DevOps metrics like CPU and memory, fall short in such environments. In this post, we show how you can perform predictive analysis on aggregated […]

Build a real-time, low-code anomaly detection pipeline for time series data using Amazon Aurora, Amazon Redshift ML, and Amazon SageMaker

The Industrial Internet of Things (IIOT) revolution has transformed the way various industries such as manufacturing and automobile work. Industry 4.0—also called the Fourth Industrial Revolution or 4IR—is the next phase in the digitization of the manufacturing sector, driven by disruptive trends including the rise of data and connectivity, analytics, human-machine interaction, and improvements in […]

Build a generative AI-powered agent assistance application using Amazon Aurora and Amazon SageMaker JumpStart

Generative AI is a form of artificial intelligence (AI) that is designed to generate content, including text, images, video, and music. In today’s business landscape, harnessing the potential of generative AI has become essential to remain competitive. Foundation models are a form of generative AI. They generate output from one or more inputs (prompts) in […]

Building AI-powered search in PostgreSQL using Amazon SageMaker and pgvector

Organizations across diverse sectors are exploring novel ways to enhance user experiences by harnessing the potential of Generative AI and large language models (LLMs). In the fashion industry generative AI is revolutionizing the creative process. By analyzing user preferences and data, AI algorithms can generate unique apparel patterns and designs, bringing a new level of […]

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. […]

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 […]

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 […]