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AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation model | AWS Machine Learning Blog
The AFX team’s product migration to the Nova Lite model has delivered tangible enterprise value by enhancing sales workflows. By migrating to the Amazon Nova Lite model, the team has not only achieved significant cost savings and reduced latency, but has also empowered sellers with a leading intelligent and reliable solution.
Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale | AWS Machine Learning Blog
In this post, the AWS and Cisco teams unveil a new methodical approach that addresses the challenges of enterprise-grade SQL generation. The teams were able to reduce the complexity of the NL2SQL process while delivering higher accuracy and better overall performance.
Amazon SageMaker Lakehouse now supports attribute-based access control | AWS Big Data Blog
Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC.
Combine keyword and semantic search for text and images using Amazon Bedrock and Amazon OpenSearch Service | AWS Machine Learning Blog
In this post, we walk you through how to build a hybrid search solution using OpenSearch Service powered by multimodal embeddings from the Amazon Titan Multimodal Embeddings G1 model through Amazon Bedrock. This solution demonstrates how you can enable users to submit both text and images as queries to retrieve relevant results from a sample retail image dataset.
Protect sensitive data in RAG applications with Amazon Bedrock | AWS Machine Learning Blog
In this post, we explore two approaches for securing sensitive data in RAG applications using Amazon Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an Amazon Bedrock knowledge base, and the second demonstrated a fine-grained RBAC pattern for managing access to sensitive information during retrieval. These solutions represent just two possible approaches among many for securing sensitive data in generative AI applications.
Accuracy evaluation framework for Amazon Q Business – Part 2 | AWS Machine Learning Blog
In the first post of this series, we introduced a comprehensive evaluation framework for Amazon Q Business, a fully managed Retrieval Augmented Generation (RAG) solution that uses your company’s proprietary data without the complexity of managing large language models (LLMs). The first post focused on selecting appropriate use cases, preparing data, and implementing metrics to […]
How Infosys improved accessibility for Event Knowledge using Amazon Nova Pro, Amazon Bedrock and Amazon Elemental Media Services | AWS Machine Learning Blog
In this post, we explore how Infosys developed Infosys Event AI to unlock the insights generated from events and conferences. Through its suite of features—including real-time transcription, intelligent summaries, and an interactive chat assistant—Infosys Event AI makes event knowledge accessible and provides an immersive engagement solution for the attendees, during and after the event.
Accelerate data pipeline creation with the new visual interface in Amazon OpenSearch Ingestion | AWS Big Data Blog
Today, we’re launching a new visual interface for OpenSearch Ingestion that makes it simple to create and manage your data pipelines from the AWS Management Console. With this new feature, you can build pipelines in minutes without writing complex configurations manually. In this post, we walk through how these new features work and how you can use them to accelerate your data ingestion projects.
Supercharge your LLM performance with Amazon SageMaker Large Model Inference container v15 | AWS Machine Learning Blog
Today, we’re excited to announce the launch of Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4 with support for the vLLM V1 engine. This release introduces significant performance improvements, expanded model compatibility with multimodality (that is, the ability to understand and analyze text-to-text, images-to-text, and text-to-images data), and provides built-in integration with vLLM to help you seamlessly deploy and serve large language models (LLMs) with the highest performance at scale.
Use Amazon Bedrock Intelligent Prompt Routing for cost and latency benefits | AWS Machine Learning Blog
Today, we’re happy to announce the general availability of Amazon Bedrock Intelligent Prompt Routing. In this blog post, we detail various highlights from our internal testing, how you can get started, and point out some caveats and best practices. We encourage you to incorporate Amazon Bedrock Intelligent Prompt Routing into your new and existing generative AI applications.
Amazon Bedrock Prompt Optimization Drives LLM Applications Innovation for Yuewen Group | AWS Machine Learning Blog
Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this blog post, we discuss how Prompt Optimization improves the performance of large language models (LLMs) for intelligent text processing task in Yuewen Group.
Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode | AWS Big Data Blog
In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables.
Migrate SQL Server user databases from Amazon EC2 to Amazon RDS Custom using Amazon EBS snapshots | AWS Database Blog
In this post, we present a practical approach to one of the most significant challenges organizations face when adopting Amazon RDS Custom for SQL Server: migrating large datasets from SQL Server on Amazon EC2 to Amazon RDS Custom for SQL Server efficiently and cost-effectively. By using SQL Server’s native detach and attach method combined with EBS snapshots, you can migrate your databases without requiring Amazon S3 or AWS DMS.
Choose the right throughput strategy for Amazon DynamoDB applications | AWS Database Blog
When getting started with DynamoDB, one of the first decisions you will make is choosing between two throughput modes: on-demand and provisioned. On-demand mode is the default and recommended throughput option because it simplifies building modern, serverless applications that can start small and scale to millions of requests per second. However, choosing the right throughput strategy requires evaluating your operational needs, development velocity, and application characteristics, with cost being a key consideration. In this post, we examine both throughput modes in detail, exploring their characteristics, strengths, and ideal use cases.
Best practices to handle AWS DMS tasks during PostgreSQL upgrades | AWS Database Blog
When you decide to upgrade your PostgreSQL database which is configured as source or target for an ongoing AWS DMS task, it’s important to factor this into your upgrade planning. In this post, we discuss the best practices to handle the AWS DMS tasks during PostgreSQL upgrades to minor or major versions.
Build an automated generative AI solution evaluation pipeline with Amazon Nova | AWS Machine Learning Blog
In this post, we explore the importance of evaluating LLMs in the context of generative AI applications, highlighting the challenges posed by issues like hallucinations and biases. We introduced a comprehensive solution using AWS services to automate the evaluation process, allowing for continuous monitoring and assessment of LLM performance. By using tools like the FMeval Library, Ragas, LLMeter, and Step Functions, the solution provides flexibility and scalability, meeting the evolving needs of LLM consumers.
Stream ingest data from Kafka to Amazon Bedrock Knowledge Bases using custom connectors | AWS Machine Learning Blog
For this post, we implement a RAG architecture with Amazon Bedrock Knowledge Bases using a custom connector and topics built with Amazon Managed Streaming for Apache Kafka (Amazon MSK) for a user who may be interested to understand stock price trends.
Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model | AWS Machine Learning Blog
In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.
Accelerate your analytics with Amazon S3 Tables and Amazon SageMaker Lakehouse | AWS Big Data Blog
Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.
How Salesforce achieves high-performance model deployment with Amazon SageMaker AI | AWS Machine Learning Blog
This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large […]
Automate video insights for contextual advertising using Amazon Bedrock Data Automation | AWS Machine Learning Blog
Amazon Bedrock Data Automation (BDA) is a new managed feature powered by FMs in Amazon Bedrock. BDA extracts structured outputs from unstructured content—including documents, images, video, and audio—while alleviating the need for complex custom workflows. In this post, we demonstrate how BDA automatically extracts rich video insights such as chapter segments and audio segments, detects text in scenes, and classifies Interactive Advertising Bureau (IAB) taxonomies, and then uses these insights to build a nonlinear ads solution to enhance contextual advertising effectiveness.
Add Zoom as a data accessor to your Amazon Q index | AWS Machine Learning Blog
This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly within their Zoom interface, alleviating the need to switch between applications while maintaining enterprise security boundaries. Organizations can now configure Zoom as a data accessor in Amazon Q Business, enabling seamless integration between their Amazon Q index and Zoom AI Companion. This integration allows users to access their enterprise knowledge in a controlled manner directly within the Zoom platform.
The future of quality assurance: Shift-left testing with QyrusAI and Amazon Bedrock | AWS Machine Learning Blog
In this post, we explore how QyrusAI and Amazon Bedrock are revolutionizing shift-left testing, enabling teams to deliver better software faster. Amazon Bedrock is a fully managed service that allows businesses to build and scale generative AI applications using foundation models (FMs) from leading AI providers. It enables seamless integration with AWS services, offering customization, security, and scalability without managing infrastructure.
Integrate your Spring Boot application with Amazon ElastiCache | AWS Database Blog
In this post, we explore the basics of integrating a Spring Boot application with ElastiCache to enable caching. Amazon ElastiCache is a fully managed, Valkey-, Memcached-, and Redis OSS-compatible service that delivers real-time, cost-optimized performance for modern applications with 99.99% SLA availability. ElastiCache speeds up application performance, scaling to millions of operations per second with microsecond response time.
Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow | AWS Machine Learning Blog
In this post, we demonstrate how to orchestrate multiple Amazon Bedrock agents to create a sophisticated Amazon EKS troubleshooting system. By enabling collaboration between specialized agents—deriving insights from K8sGPT and performing actions through the ArgoCD framework—you can build a comprehensive automation that identifies, analyzes, and resolves cluster issues with minimal human intervention.
Host concurrent LLMs with LoRAX | AWS Machine Learning Blog
In this post, we explore how Low-Rank Adaptation (LoRA) can be used to address these challenges effectively. Specifically, we discuss using LoRA serving with LoRA eXchange (LoRAX) and Amazon Elastic Compute Cloud (Amazon EC2) GPU instances, allowing organizations to efficiently manage and serve their growing portfolio of fine-tuned models, optimize costs, and provide seamless performance for their customers.
Build a computer vision-based asset inventory application with low or no training | AWS Machine Learning Blog
In this post, we present a solution using generative AI and large language models (LLMs) to alleviate the time-consuming and labor-intensive tasks required to build a computer vision application, enabling you to immediately start taking pictures of your asset labels and extract the necessary information to update the inventory using AWS services
How Amazon Finance Automation built an operational data store with AWS purpose built databases to power critical finance applications | AWS Database Blog
In this post, we discuss how the Amazon Finance Automation team used AWS purpose built databases, such as Amazon DynamoDB, Amazon OpenSearch Service, and Amazon Neptune together coupled with serverless compute like AWS Lambda to build an Operational Data Store (ODS) to store financial transactional data and support FinOps applications with millisecond latency. This data is the key enabler for FinOps business.
Clario enhances the quality of the clinical trial documentation process with Amazon Bedrock | AWS Machine Learning Blog
The collaboration between Clario and AWS demonstrated the potential of AWS AI and machine learning (AI/ML) services and generative AI models, such as Anthropic’s Claude, to streamline document generation processes in the life sciences industry and, specifically, for complicated clinical trial processes.
Optimizing Mixtral 8x7B on Amazon SageMaker with AWS Inferentia2 | AWS Machine Learning Blog
This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We'll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.
Dynamic text-to-SQL for enterprise workloads with Amazon Bedrock Agents | AWS Machine Learning Blog
This post demonstrates how enterprises can implement a scalable agentic text-to-SQL solution using Amazon Bedrock Agents, with advanced error-handling tools and automated schema discovery to enhance database query efficiency.
Build multi-agent systems with LangGraph and Amazon Bedrock | AWS Machine Learning Blog
This post demonstrates how to integrate open-source multi-agent framework, LangGraph, with Amazon Bedrock. It explains how to use LangGraph and Amazon Bedrock to build powerful, interactive multi-agent applications that use graph-based orchestration.
Racing beyond DeepRacer: Debut of the AWS LLM League | AWS Machine Learning Blog
The AWS LLM League was designed to lower the barriers to entry in generative AI model customization by providing an experience where participants, regardless of their prior data science experience, could engage in fine-tuning LLMs. Using Amazon SageMaker JumpStart, attendees were guided through the process of customizing LLMs to address real business challenges adaptable to their domain.
Building an AIOps chatbot with Amazon Q Business custom plugins | AWS Machine Learning Blog
In this post, we demonstrate how you can use custom plugins for Amazon Q Business to build a chatbot that can interact with multiple APIs using natural language prompts. We showcase how to build an AIOps chatbot that enables users to interact with their AWS infrastructure through natural language queries and commands. The chatbot is capable of handling tasks such as querying the data about Amazon Elastic Compute Cloud (Amazon EC2) ports and Amazon Simple Storage Service (Amazon S3) buckets access settings.
How TransPerfect Improved Translation Quality and Efficiency Using Amazon Bedrock | AWS Machine Learning Blog
This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate Amazon Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.
How Heroku migrated hundreds of thousands of self-managed PostgreSQL databases to Amazon Aurora | AWS Database Blog
In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Aurora PostgreSQL-Compatible Edition. Heroku completed this migration with no customer impact, increasing platform reliability while simultaneously reducing operational burden. We dive into Heroku and their previous self-managed architecture, the new architecture, how the migration of hundreds of thousands of databases was performed, and the enhancements to the customer experience since its completion.