News
Entertainment
Science & Technology
Life
Culture & Art
Hobbies
News
Entertainment
Science & Technology
Culture & Art
Hobbies
Bridging the gap between development and production: Seamless model lifecycle management with Amazon Bedrock | AWS Machine Learning Blog
Amazon Bedrock Model Copy and Model Share features provide a powerful option for managing the lifecycle of an AI application from development to production. In this comprehensive blog post, we'll dive deep into the Model Share and Model Copy features, exploring their functionalities, benefits, and practical applications in a typical development-to-production scenario.
PackScan: Building real-time sort center analytics with AWS Services | AWS Big Data Blog
In this post, we explore how PackScan uses Amazon cloud-based services to drive real-time visibility, improve logistics efficiency, and support the seamless movement of packages across Amazon’s Middle Mile network.
Architect a mature generative AI foundation on AWS | AWS Machine Learning Blog
In this post, we give an overview of a well-established generative AI foundation, dive into its components, and present an end-to-end perspective. We look at different operating models and explore how such a foundation can operate within those boundaries. Lastly, we present a maturity model that helps enterprises assess their evolution path.
Using Amazon OpenSearch ML connector APIs | AWS Machine Learning Blog
OpenSearch offers a wide range of third-party machine learning (ML) connectors to support this augmentation. This post highlights two of these third-party ML connectors. The first connector we demonstrate is the Amazon Comprehend connector. In this post, we show you how to use this connector to invoke the LangDetect API to detect the languages of ingested documents. The second connector we demonstrate is the Amazon Bedrock connector to invoke the Amazon Titan Text Embeddings v2 model so that you can create embeddings from ingested documents and perform semantic search.
Build a multi-Region session store with Amazon ElastiCache for Valkey Global Datastore | AWS Database Blog
As companies expand globally, they must be able to architect highly available and fault-tolerant systems across multiple AWS Regions. With such scale, a company can find itself in this position when designing a caching solution across its multi-Region infrastructure. In this post, we dive deep into how to use Amazon ElastiCache for Valkey, a fully managed in-memory data store with Redis OSS and Valkey compatibility, and the Amazon ElastiCache for Valkey Global Datastore feature set.
Deploy Amazon SageMaker Projects with Terraform Cloud | AWS Machine Learning Blog
In this post you define, deploy, and provision a SageMaker Project custom template purely in Terraform. With no dependencies on other IaC tools, you can now enable SageMaker Projects strictly within your Terraform Enterprise infrastructure.
How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker | AWS Machine Learning Blog
ZURU collaborated with AWS Generative AI Innovation Center and AWS Professional Services to implement a more accurate text-to-floor plan generator using generative AI. In this post, we show you why a solution using a large language model (LLM) was chosen. We explore how model selection, prompt engineering, and fine-tuning can be used to improve results.
Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI | AWS Machine Learning Blog
Non-conversational applications offer unique advantages such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision. This post examines four diverse Amazon.com examples of such generative AI applications.
Real-world applications of Amazon Nova Canvas for interior design and product photography | AWS Machine Learning Blog
In this post, we explore how Amazon Nova Canvas can solve real-world business challenges through advanced image generation techniques. We focus on two specific use cases that demonstrate the power and flexibility of this technology: interior design and product photography.
Create an agentic RAG application for advanced knowledge discovery with LlamaIndex, and Mistral in Amazon Bedrock | AWS Machine Learning Blog
In this post, we demonstrate an example of building an agentic RAG application using the LlamaIndex framework. LlamaIndex is a framework that connects FMs with external data sources. It helps ingest, structure, and retrieve information from databases, APIs, PDFs, and more, enabling the agent and RAG for AI applications. This application serves as a research tool, using the Mistral Large 2 FM on Amazon Bedrock generate responses for the agent flow.
How Airties achieved scalability and cost-efficiency by moving from Kafka to Amazon Kinesis Data Streams | AWS Big Data Blog
Airties is a wireless networking company that provides AI-driven solutions for enhancing home connectivity. This post explores the strategies the Airties team employed during their migration from Apache Kafka to Amazon Kinesis Data Streams, the challenges they overcame, and how they achieved a more efficient, scalable, and maintenance-free streaming infrastructure.
Text-to-image basics with Amazon Nova Canvas | AWS Machine Learning Blog
In this post, we focus on the Amazon Nova Canvas image generation model. We then provide an overview of the image generation process (diffusion) and dive deep into the input parameters for text-to-image generation with Amazon Nova Canvas.
Building a multimodal RAG based application using Amazon Bedrock Data Automation and Amazon Bedrock Knowledge Bases | AWS Machine Learning Blog
In this post, we walk through building a full-stack application that processes multimodal content using Amazon Bedrock Data Automation, stores the extracted information in an Amazon Bedrock knowledge base, and enables natural language querying through a RAG-based Q&A interface.
Tailoring foundation models for your business needs: A comprehensive guide to RAG, fine-tuning, and hybrid approaches | AWS Machine Learning Blog
In this post, we show you how to implement and evaluate three powerful techniques for tailoring FMs to your business needs: RAG, fine-tuning, and a hybrid approach combining both methods. We provid ready-to-use code to help you experiment with these approaches and make informed decisions based on your specific use case and dataset.
Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL | AWS Database Blog
In this post, we explore how pgvector 0.8.0 on Aurora PostgreSQL-Compatible delivers up to 9x faster query processing and 100x more relevant search results, addressing key scaling challenges that enterprise AI applications face when implementing vector search at scale.
How Rufus doubled their inference speed and handled Prime Day traffic with AWS AI chips and parallel decoding | AWS Machine Learning Blog
Rufus, an AI-powered shopping assistant, relies on many components to deliver its customer experience including a foundation LLM (for response generation) and a query planner (QP) model for query classification and retrieval enhancement. This post focuses on how the QP model used draft centric speculative decoding (SD)—also called parallel decoding—with AWS AI chips to meet the demands of Prime Day. By combining parallel decoding with AWS Trainium and Inferentia chips, Rufus achieved two times faster response times, a 50% reduction in inference costs, and seamless scalability during peak traffic.
A generative AI prototype with Amazon Bedrock transforms life sciences and the genome analysis process | AWS Machine Learning Blog
This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. We demonstrate how to implement an AI assistant web interface with AWS Amplify and explain the prompt engineering strategies adopted to generate the SQL queries. Finally, we present instructions to deploy the service in your own AWS account.
Unlock self-serve streaming SQL with Amazon Managed Service for Apache Flink | AWS Big Data Blog
In this post, we present Riskified’s journey toward enabling self-service streaming SQL pipelines. We walk through the motivations behind the shift from Confluent ksqlDB to Apache Flink, the architecture Riskified built using Amazon Managed Service for Apache Flink, the technical challenges they faced, and the solutions that helped them make streaming accessible, scalable, and production-ready.
Part 3: Building an AI-powered assistant for investment research with multi-agent collaboration in Amazon Bedrock and Amazon Bedrock Data Automation | AWS Machine Learning Blog
In this post, we walk through how to build a multi-agent investment research assistant using the multi-agent collaboration capability of Amazon Bedrock. Our solution demonstrates how a team of specialized AI agents can work together to analyze financial news, evaluate stock performance, optimize portfolio allocations, and deliver comprehensive investment insights—all orchestrated through a unified, natural language interface.
Automate Amazon RDS for PostgreSQL major or minor version upgrade using AWS Systems Manager and Amazon EC2 | AWS Database Blog
In this post, we guide you through setting up automation for pre-upgrade checks and upgrading a fleet of Amazon RDS for PostgreSQL instances. In this solution, we use AWS Systems Manager to automate the Amazon RDS upgrade job.
Gemma 3 27B model now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart | AWS Machine Learning Blog
We are excited to announce the availability of Gemma 3 27B Instruct models through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. In this post, we show you how to get started with Gemma 3 27B Instruct on both Amazon Bedrock Marketplace and SageMaker JumpStart, and how to use the model’s powerful instruction-following capabilities in your applications.
New Amazon Bedrock Data Automation capabilities streamline video and audio analysis | AWS Machine Learning Blog
Amazon Bedrock Data Automation helps organizations streamline development and boost efficiency through customizable, multimodal analytics. It eliminates the heavy lifting of unstructured content processing at scale, whether for video or audio. The new capabilities make it faster to extract tailored, generative AI-powered insights like scene summaries, key topics, and customer intents from video and audio. This unlocks the value of unstructured content for use cases such as improving sales productivity and enhancing customer experience.
GuardianGamer scales family-safe cloud gaming with AWS | AWS Machine Learning Blog
In this post, we share how GuardianGamer uses AWS services including Amazon Nova and Amazon Bedrock to deliver a scalable and efficient supervision platform. The team uses Amazon Nova for intelligent narrative generation to provide parents with meaningful insights into their children’s gaming activities and social interactions, while maintaining a non-intrusive approach to monitoring.
Unify streaming and analytical data with Amazon Data Firehose and Amazon SageMaker Lakehouse | AWS Big Data Blog
In this post, we show you how to create Iceberg tables in Amazon SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.
Explore the new openCypher custom functions and subquery support in Amazon Neptune | AWS Database Blog
In this post, we describe some of the openCypher features that have been released as part of the 1.4.2.0 engine update to Amazon Neptune. Neptune provides developers with the choice of building their graph applications using three open graph query languages: openCypher, Apache TinkerPop Gremlin, and the World Wide Web Consortium’s (W3C) SPARQL 1.1. You can use the guide at the end of this post to try out the new features that are described.
Run SQL Server post-migration activities using Cloud Migration Factory on AWS | AWS Database Blog
In this post, we show you essential post-migration tasks to perform after migrating your SQL Server database to Amazon EC2 and how to automate this activity by using Cloud Migration Factory on AWS (CMF), such as validating database status, configuring performance settings, and running consistency checks. Additionally, we explore how the CMF solution can automate these essential tasks, providing efficiency, scalability, and heightened visibility to simplify and expedite your migration process.
OpenSearch UI: Six months in review | AWS Big Data Blog
OpenSearch UI has been adopted by thousands of customers for various use cases since its launch in November 2024. Exciting customer stories and feedback have helped shape our feature improvements. As we complete 6 months since its general availability, we are sharing major enhancements that have improved OpenSearch UI’s capability, especially in observability and security analytics, in this post.
Connect Amazon Bedrock Agents with Amazon Aurora PostgreSQL using Amazon RDS Data API | AWS Database Blog
In this post, we describe a solution to integrate generative AI applications with relational databases like Amazon Aurora PostgreSQL-Compatible Edition using RDS Data API (Data API) for simplified database interactions, Amazon Bedrock for AI model access, Amazon Bedrock Agents for task automation and Amazon Bedrock Knowledge Bases for context information retrieval.
Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight | AWS Machine Learning Blog
In this post, we explore how Principal used this opportunity to build an integrated voice VA reporting and analytics solution using an Amazon QuickSight dashboard.
Boosting team productivity with Amazon Q Business Microsoft 365 integrations for Microsoft 365 Outlook and Word | AWS Machine Learning Blog
Amazon Q Business integration with Microsoft 365 applications offers powerful AI assistance directly within the tools that your team already uses daily. In this post, we explore how these integrations for Outlook and Word can transform your workflow.
Amazon Aurora Global Database introduces support for up to 10 secondary Regions | AWS Database Blog
In this post, we dive deep into Amazon Aurora Global Database's new support for up to 10 secondary Regions and explore use cases it unlocks. An Aurora Global Database consists of one primary Region and up to 10 read-only secondary Regions for low-latency local reads.
Scalable analytics and centralized governance for Apache Iceberg tables using Amazon S3 Tables and Amazon Redshift | AWS Big Data Blog
In this post, we’ll build on the first post in this series to show you how to set up an Apache Iceberg data lake catalog using Amazon S3 Tables and provide different levels of access control to your data. Through this example, you’ll set up fine-grained access controls for multiple users and see how this works using Amazon Redshift. We’ll also review an example with simultaneously using data that resides both in Amazon Redshift and Amazon S3 Tables, enabling a unified analytics experience.
Optimize query responses with user feedback using Amazon Bedrock embedding and few-shot prompting | AWS Machine Learning Blog
This post demonstrates how Amazon Bedrock, combined with a user feedback dataset and few-shot prompting, can refine responses for higher user satisfaction. By using Amazon Titan Text Embeddings v2, we demonstrate a statistically significant improvement in response quality, making it a valuable tool for applications seeking accurate and personalized responses.
Integrate Amazon Bedrock Agents with Slack | AWS Machine Learning Blog
In this post, we present a solution to incorporate Amazon Bedrock Agents in your Slack workspace. We guide you through configuring a Slack workspace, deploying integration components in Amazon Web Services, and using this solution.
How to configure a Linked Server between Amazon RDS for SQL Server and Teradata database | AWS Database Blog
In this post, we demonstrate how to configure a linked server between Amazon RDS for SQL Server and a Teradata database instance. We guide you through the step-by-step process to establish this connection and show you how to verify its functionality.
Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach | AWS Machine Learning Blog
In this post, we introduce a multi-agent collaboration pipeline for processing unstructured insurance data using Amazon Bedrock, featuring specialized agents for classification, conversion, and metadata extraction. We demonstrate how this domain-aware approach transforms diverse data formats like claims documents, videos, and audio files into metadata-rich outputs that enable fraud detection, customer 360-degree views, and advanced analytics.
Empower financial analytics by creating structured knowledge bases using Amazon Bedrock and Amazon Redshift | AWS Big Data Blog
In this post, we showcase how financial planners, advisors, or bankers can now ask questions in natural language. These prompts will receive precise data from the customer databases for accounts, investments, loans, and transactions. Amazon Bedrock Knowledge Bases automatically translates these natural language queries into optimized SQL statements, thereby accelerating time to insight, enabling faster discoveries and efficient decision-making.
Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances | AWS Database Blog
In this post, we demonstrate how upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 on Aurora I/O-Optimized cluster configuration can deliver significant price-performance gains – delivering up to 1.7 times higher write throughput, 1.38 times better price-performance and reducing commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances as compared to Graviton2-based R6g instances.