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AWS Machine Learning

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AWS Machine Learning

02.04.2025

Using StatsD for monitoring Oracle databases running on Amazon RDS or Amazon EC2 | AWS Database Blog

Monitoring databases is essential in large IT environments to prevent potential issues from becoming major problems that can result in data loss or downtime. Having custom dashboards and alarm-based monitoring for the database can help in analyzing historical metrics patterns and improve database availability by alerting users of any abnormal threshold breaches. In this post, we show you how to set up monitoring for your Oracle database using StatsD.

AWS Machine Learning

03.04.2025

Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI | AWS Machine Learning Blog

Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. In this post, we demonstrate the steps involved in running Ray jobs on SageMaker HyperPod.

AWS Machine Learning

03.04.2025

Using Large Language Models on Amazon Bedrock for multi-step task execution | AWS Machine Learning Blog

This post explores the application of LLMs in executing complex analytical queries through an API, with specific focus on Amazon Bedrock. To demonstrate this process, we present a use case where the system identifies the patient with the least number of vaccines by retrieving, grouping, and sorting data, and ultimately presenting the final result.

AWS Machine Learning

03.04.2025

Improve PostgreSQL performance using the pgstattuple extension | AWS Database Blog

In this post, we explore the pgstattuple extension in depth; what insights it offers, how to use it to diagnose issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL, and best practices for harnessing its capabilities.

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