AWS Storage Blog
Category: Artificial Intelligence
Enabling natural language access to structured data using Amazon S3 Tables and Amazon Bedrock Knowledge Bases
Organizations generate massive volumes of structured data from customer transactions, operational metrics, product catalogs, and compliance records. This data contains insights that can help businesses make better and timely decisions. Financial advisors need to review client transaction histories, retail analysts track inventory trends, and healthcare administrators monitor patient outcomes. Yet accessing these insights creates a […]
Migrate to Amazon S3 account regional namespaces
Since its launch in 2006, Amazon S3 has used a global namespace where bucket names must be unique across all AWS accounts and AWS Regions. This design has served customers well at scale, but organizations managing multiple accounts and environments often encounter naming collisions. When a bucket is deleted, its name returns to the global […]
Troubleshooting Amazon S3 access denied errors using Kiro CLI
Managing data access across multiple layers of permissions is a common industry challenge. Changes to AWS Identity and Access Management (AWS IAM) policies, Amazon Simple Storage Service (Amazon S3) bucket configurations, AWS Key Management Service (AWS KMS) key policies, or Amazon Virtual Private Cloud (Amazon VPC) endpoint policies can unintentionally cause access issues. When these […]
Optimize agent tool selection using Amazon S3 Vectors and Amazon Bedrock Knowledge Bases
State-of-the-art AI agents rely on external tools to perform actions on their behalf. A tool is a function with a clear description, defined inputs, and outputs that extend the capabilities of a large language model (LLM). As toolkits expand, selecting the right tool for each task requires effective mechanisms, among which semantic search enables agents […]
Architecting high performance AI-driven data applications with Spice AI and AWS
As enterprises scale their adoption of generative AI, one of the biggest technical challenges is connecting AI applications to the right data and making that data fast, accessible, and secure. AI agents are transforming industries through applications like customer support automation, personalized e-commerce recommendations, and research assistance in financial services and healthcare. These applications require […]
Optimizing recommendations and analytics using Amazon DynamoDB and Amazon S3
Today, consumers navigate thousands of products on e-commerce sites, hundreds of shows on streaming platforms, and countless options in digital marketplaces. This choice overload creates decision fatigue, yet consumers continue to demand more variety and make more purchases online. As a result, personalization has become essential—consumers reward brands that deliver relevant, tailored online experiences. However, […]
Building self-managed RAG applications with Amazon EKS and Amazon S3 Vectors
Retrieval-Augmented Generation (RAG) is a technique that optimizes large language model (LLM) outputs by referencing authoritative knowledge bases outside of the model’s training data before generating responses. This addresses common limitations of traditional LLMs, such as outdated knowledge, hallucinated facts, and misinterpreted terminology. Organizations can implement RAG to enhance their generative AI applications with current, […]
Enhancing FSx for Windows security: AI-powered anomaly detection
In today’s rapidly evolving threat landscape, Security Operations Center (SOC) teams face significant challenges in efficiently analyzing audit logs to identify potential security breaches in cloud file systems. Amazon FSx for Windows File Server generates comprehensive audit logs capturing detailed user activities, but traditional manual analysis of these logs is time-consuming, resource-intensive, and often ineffective […]
Implementing conversational AI for S3 Tables using Model Context Protocol (MCP)
In today’s data-driven world, the ability to interact with your data through natural language is becoming increasingly valuable. By combining the power of conversational AI with Amazon S3 Tables, organizations can democratize data access and enable individuals across technical skill levels to query, analyze, and gain insights from their data using simple conversations. Model Context […]
Architecting scalable checkpoint storage for large-scale ML training on AWS
The exponential growth in size and complexity of foundation models (FMs) has created unprecedented infrastructure demands across compute, networking, and storage resources. Storage systems, in particular, face intense requirements for throughput, latency, and capacity. In machine learning (ML) model training, these storage demands are particularly evident in checkpointing—a critical reliability mechanism that periodically saves and […]




