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Innovating at speed: BMW’s generative AI solution for cloud incident analysis | AWS Machine Learning Blog
In this post, we explain how BMW uses generative AI to speed up the root cause analysis of incidents in complex and distributed systems in the cloud such as BMW’s Connected Vehicle backend serving 23 million vehicles. Read on to learn how the solution, collaboratively pioneered by AWS and BMW, uses Amazon Bedrock Agents and Amazon CloudWatch logs and metrics to find root causes quicker. This post is intended for cloud solution architects and developers interested in speeding up their incident workflows.
Time series forecasting with LLM-based foundation models and scalable AIOps on AWS | AWS Machine Learning Blog
In this blog post, we will guide you through the process of integrating Chronos into Amazon SageMaker Pipeline using a synthetic dataset that simulates a sales forecasting scenario, unlocking accurate and efficient predictions with minimal data.
Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval | AWS Machine Learning Blog
In this post, we discuss best practices for applying LLMs to generate ground truth for evaluating question-answering assistants with FMEval on an enterprise scale. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify, and provides standardized implementations of metrics to assess quality and responsibility. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.
Accelerate AWS Well-Architected reviews with Generative AI | AWS Machine Learning Blog
In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process. We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This solution automates portions of the WAFR report creation, helping solutions architects improve the efficiency and thoroughness of architectural assessments while supporting their decision-making process.
Dynamic metadata filtering for Amazon Bedrock Knowledge Bases with LangChain | AWS Machine Learning Blog
Amazon Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search. In this post, we discuss using metadata filters with Amazon Bedrock Knowledge Bases.
Amazon OpenSearch Service vector database capabilities revisited | AWS Big Data Blog
As we enter 2025, OpenSearch Service support for OpenSearch 2.17 brings these improvements to the service. In this post, we walk through 2024’s innovations with an eye to how you can adopt new features to lower your cost, reduce your latency, and improve the accuracy of your search results and generated text.
Reduce conversational AI response time through inference at the edge with AWS Local Zones | AWS Machine Learning Blog
This guide demonstrates how to deploy an open source foundation model from Hugging Face on Amazon EC2 instances across three locations: a commercial AWS Region and two AWS Local Zones. Through comparative benchmarking tests, we illustrate how deploying foundation models in Local Zones closer to end users can significantly reduce latency—a critical factor for real-time applications such as conversational AI assistants.
Customize DeepSeek-R1 distilled models using Amazon SageMaker HyperPod recipes – Part 1 | AWS Machine Learning Blog
In this two-part series, we discuss how you can reduce the DeepSeek model customization complexity by using the pre-built fine-tuning workflows (also called “recipes”) for both DeepSeek-R1 model and its distilled variations, released as part of Amazon SageMaker HyperPod recipes. In this first post, we will build a solution architecture for fine-tuning DeepSeek-R1 distilled models and demonstrate the approach by providing a step-by-step example on customizing the DeepSeek-R1 Distill Qwen 7b model using recipes, achieving an average of 25% on all the Rouge scores, with a maximum of 49% on Rouge 2 score with both SageMaker HyperPod and SageMaker training jobs. The second part of the series will focus on fine-tuning the DeepSeek-R1 671b model itself.
Customer-managed process for configuring Kerberos authentication on an Amazon RDS for SQL Server DB instance, joined to a self-managed Active Directory | AWS Database Blog
Many organizations rely on Windows Authentication and Kerberos for secure access to their SQL Server databases. When using Amazon RDS for SQL Server with a self-managed Active Directory, organizations can enhance their authentication beyond the default NTLM protocol to support Kerberos authentication. In this post, we show you how to manually configure and maintain Kerberos authentication for Amazon RDS for SQL Server DB instances joined to a self-managed Active Directory. We walk through the process of configuring service principal names (SPNs), adding necessary user principal name (UPN) suffixes, and automating SPN updates to handle failovers and host replacements.
Automate Amazon RDS credential rotation with AWS Secrets Manager for primary instances with read replicas | AWS Database Blog
When using Secrets Manager to manage your master user passwords, you cannot create new read replicas for your database instance. This applies to all DB engines except Amazon RDS for SQL Server, potentially impacting your organization’s ability to efficiently scale its read operations while maintaining secure credential practices. In this post, we present a solution that automates the process of rotating passwords for a primary instance with read replicas while maintaining secure credential management practices. This approach allows you to take advantage of the benefits of both read scaling and automated credential rotation.
Design patterns for implementing Hive Metastore for Amazon EMR on EKS | AWS Big Data Blog
In this post, we explore the design patterns for implementing the Hive Metastore (HMS) with EMR on EKS with Spark Operator, each offering distinct advantages depending on your requirements. Whether you choose to deploy HMS as a sidecar container within the Apache Spark Driver pod, or as a Kubernetes deployment in the data processing EKS cluster, or as an external HMS service in a separate EKS cluster, the key considerations revolve around communication efficiency, scalability, resource isolation, high availability, and security.
Level up your problem-solving and strategic thinking skills with Amazon Bedrock | AWS Machine Learning Blog
In this post, we show how Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock can be used for a variety of business-related cognitive tasks, such as problem-solving, critical thinking and ideation—to help augment human thinking and improve decision-making among knowledge workers to accelerate innovation.
The end of an era: the final AWS DeepRacer League Championship at re:Invent 2024 | AWS Machine Learning Blog
The AWS DeepRacer League is the world's first global autonomous racing league powered by machine learning (ML). Over the past 6 years, a diverse community of over 560,000 builders from more than 150 countries worldwide have participated in the League to learn ML fundamentals hands-on through the fun of friendly autonomous racing. After an 8-month season of nail-biting virtual qualifiers, finalists convened in person at re:Invent in Las Vegas for one final showdown to compete for prizes and glory in the high-stakes, winner-take-all AWS DeepRacer League Championship.
Optimizing AI implementation costs with Automat-it | AWS Machine Learning Blog
In this guest post, we explain how AWS Partner Automat-it helped their customer achieve a more than twelvefold cost savings while keeping AI model performance within the required performance thresholds. This was accomplished through careful tuning of architecture, algorithm selection, and infrastructure management.
Streamline work insights with the Amazon Q Business connector for Smartsheet | AWS Machine Learning Blog
This post explains how to integrate Smartsheet with Amazon Q Business to use natural language and generative AI capabilities for enhanced insights. Smartsheet, the AI-enhanced enterprise-grade work management platform, helps users manage projects, programs, and processes at scale.
Governing streaming data in Amazon DataZone with the Data Solutions Framework on AWS | AWS Big Data Blog
In this post, we explore how AWS customers can extend Amazon DataZone to support streaming data such as Amazon Managed Streaming for Apache Kafka (Amazon MSK) topics. Developers and DevOps managers can use Amazon MSK, a popular streaming data service, to run Kafka applications and Kafka Connect connectors on AWS without becoming experts in operating it.
Evaluate healthcare generative AI applications using LLM-as-a-judge on AWS | AWS Machine Learning Blog
In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.
Amazon Prime Video advances search for sports using Amazon OpenSearch Service | AWS Big Data Blog
In this post, we will walk you through how Prime Video used Amazon OpenSearch Service and its AI and machine learning (AI/ML) capabilities to build a more intuitive and enhanced sports search experience.
AWS DeepRacer: Closing time at AWS re:Invent 2024 –How did that physical racing go? | AWS Machine Learning Blog
In AWS DeepRacer: How to master physical racing?, I wrote in detail about some aspects relevant to racing AWS DeepRacer in the physical world. The previous post was left open-ended—with one last Championship Final left, it was too early to share all my secrets. Now that AWS re:Invent is over, it’s time to share my strategy, how I prepared, and how it went in the end.
Amazon Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools | AWS Big Data Blog
Amazon Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. We were positioned in the Challengers Quadrant in 2023. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.
How to configure cross-account model deployment using Amazon Bedrock Custom Model Import | AWS Machine Learning Blog
In this guide, we walk you through step-by-step instructions for configuring cross-account access for Amazon Bedrock Custom Model Import, covering both non-encrypted and AWS Key Management Service (AWS KMS) based encrypted scenarios.
Upgrade strategies for Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL 12 | AWS Database Blog
In this post, we explore the end-of-life (EOL) timeline for Aurora PostgreSQL and Amazon RDS for PostgreSQL. We discuss features in PostgreSQL major versions, Amazon RDS Extended Support, and various upgrade strategies, including in-place upgrades, Amazon RDS blue/green deployments, and out-of-place upgrades.
How Pattern PXM’s Content Brief is driving conversion on ecommerce marketplaces using AI | AWS Machine Learning Blog
Pattern is a leader in ecommerce acceleration, helping brands navigate the complexities of selling on marketplaces and achieve profitable growth through a combination of proprietary technology and on-demand expertise. In this post, we share how Pattern uses AWS services to process trillions of data points to deliver actionable insights, optimizing product listings across multiple services.
ByteDance processes billions of daily videos using their multimodal video understanding models on AWS Inferentia2 | AWS Machine Learning Blog
At ByteDance, we collaborated with Amazon Web Services (AWS) to deploy multimodal large language models (LLMs) for video understanding using AWS Inferentia2 across multiple AWS Regions around the world. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day. In this post, we discuss the use of multimodal LLMs for video understanding, the solution architecture, and techniques for performance optimization.
Top analytics announcements of AWS re:Invent 2024 | AWS Big Data Blog
AWS re:Invent 2024, the flagship annual conference, took place December 2–6, 2024, in Las Vegas, bringing together thousands of cloud enthusiasts, innovators, and industry leaders from around the globe. Analytics remained one of the key focus areas this year, with significant updates and innovations aimed at helping businesses harness their data more efficiently and accelerate insights. In this post, we walk you through the top analytics announcements from re:Invent 2024 and explore how these innovations can help you unlock the full potential of your data.
How IDIADA optimized its intelligent chatbot with Amazon Bedrock | AWS Machine Learning Blog
In 2021, Applus+ IDIADA, a global partner to the automotive industry with over 30 years of experience supporting customers in product development activities through design, engineering, testing, and homologation services, established the Digital Solutions department. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using Amazon Bedrock.
Accelerate IaC troubleshooting with Amazon Bedrock Agents | AWS Machine Learning Blog
This post demonstrates how Amazon Bedrock Agents, combined with action groups and generative AI models, streamlines and accelerates the resolution of Terraform errors while maintaining compliance with environment security and operational guidelines.
Perform minor/major version upgrades for Amazon Aurora MySQL Global Database with minimum downtime | AWS Database Blog
In this post, we show how you can use the blue/green deployment technique to perform engine version upgrades for Aurora MySQL global database clusters with minimal downtime. The approach described in this post enables you to keep your global cluster topology intact and retain access to global capabilities throughout the process. Although this post focuses on version upgrades, you can use a similar approach for other types of database maintenance or change management.
Building and operating data pipelines at scale using CI/CD, Amazon MWAA and Apache Spark on Amazon EMR by Wipro | AWS Big Data Blog
This blog post discusses how a programmatic data processing framework developed by Wipro can help data engineers overcome obstacles and streamline their organization’s ETL processes. The framework leverages Amazon EMR improved runtime for Apache Spark and integrates with AWS Managed services.
How Rocket Companies modernized their data science solution on AWS | AWS Machine Learning Blog
In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.
Supercharge your RAG applications with Amazon OpenSearch Service and Aryn DocParse | AWS Big Data Blog
In this post, we demonstrate how to use Amazon OpenSearch Service with purpose-built document ETL tools, Aryn DocParse and Sycamore, to quickly build a RAG application that relies on complex documents. We use over 75 PDF reports from the National Transportation Safety Board (NTSB) about aircraft incidents. You can refer to the following example document from the collection. As you can see, these documents are complex, containing tables, images, section headings, and complicated layouts.
Improve search results for AI using Amazon OpenSearch Service as a vector database with Amazon Bedrock | AWS Big Data Blog
In this post, you'll learn how to use OpenSearch Service and Amazon Bedrock to build AI-powered search and generative AI applications. You’ll learn about how AI-powered search systems employ foundation models (FMs) to capture and search context and meaning across text, images, audio, and video, delivering more accurate results to users. You’ll learn how generative AI systems use these search results to create original responses to questions, supporting interactive conversations between humans and machines.
How Mindbody improved query latency and optimized costs using Amazon Aurora PostgreSQL Optimized Reads | AWS Database Blog
In this post, we highlight the scaling and performance challenges Mindbody was facing due to an increase in their data growth. We also present the root cause analysis and recommendations for adopting to Aurora Optimized Reads, outlining the steps taken to address these issues. Finally, we discuss the benefits Mindbody realized from implementing these changes, including enhanced query performance, significant cost savings, and improved price predictability.
LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker | AWS Machine Learning Blog
In this post, we present the continuous self-instruct fine-tuning framework as a compound AI system implemented by the DSPy framework. The framework first generates a synthetic dataset from the domain knowledge base and documents for self-instruction, then drives model fine-tuning through SFT, and introduces the human-in-the-loop workflow to collect human and AI feedback to the model response, which is used to further improve the model performance by aligning human preference through reinforcement learning (RLHF/RLAIF).
Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock | AWS Machine Learning Blog
This intelligent document processing solution uses Amazon Bedrock FMs to orchestrate a sophisticated workflow for handling multi-page healthcare documents with mixed content types. The solution uses the FM’s tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks.