AWS Machine Learning Blog
Detect Amazon Bedrock misconfigurations with Datad...
We’re excited to announce new security capabilities in Datadog Cloud Security that can help you detect and remediate Amazon Bedrock misconfigurations before they become security incidents. This integration helps organizations embed robust security controls and secure their use of...
Set up custom domain names for Amazon Bedrock Agen...
In this post, we show you how to create custom domain names for your Amazon Bedrock AgentCore Runtime agent endpoints using CloudFront as a reverse proxy. This solution provides several key benefits: simplified integration for development teams, custom domains...
Introducing auto scaling on Amazon SageMaker Hyper...
In this post, we announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, enabling efficient scaling of SageMaker HyperPod clusters to meet inference and training demands. We dive into the benefits of Karpenter and provide...
Meet Boti: The AI assistant transforming how the c...
This post describes the agentic AI assistant built by the Government of the City of Buenos Aires and the GenAIIC to respond to citizens’ questions about government procedures. The solution consists of two primary components: an input guardrail system...
Empowering air quality research with secure, ML-dr...
In this post, we provide a data imputation solution using Amazon SageMaker AI, AWS Lambda, and AWS Step Functions. This solution is designed for environmental analysts, public health officials, and business intelligence professionals who need reliable PM2.5 data for...
How Amazon Finance built an AI assistant using Ama...
The Amazon Finance technical team develops and manages comprehensive technology solutions that power financial decision-making and operational efficiency while standardizing across Amazon’s global operations. In this post, we explain how the team conceptualized and implemented a solution to these...
Mercury foundation models from Inception Labs are ...
In this post, we announce that Mercury and Mercury Coder foundation models from Inception Labs are now available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. We demonstrate how to deploy these ultra-fast diffusion-based language models that can generate...
Learn how Amazon Health Services improved discover...
In this post, we show you how Amazon Health Services (AHS) solved discoverability challenges on Amazon.com search using AWS services such as Amazon SageMaker, Amazon Bedrock, and Amazon EMR. By combining machine learning (ML), natural language processing, and vector...
Enhance Geospatial Analysis and GIS Workflows with...
Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their missions and responsibilities. In this post, we explore how you can integrate...
Beyond the basics: A comprehensive foundation mode...
As the model landscape expands, organizations face complex scenarios when selecting the right foundation model for their applications. In this blog post we present a systematic evaluation methodology for Amazon Bedrock users, combining theoretical frameworks with practical implementation strategies...
Accelerate intelligent document processing with ge...
In this post, we introduce our open source GenAI IDP Accelerator—a tested solution that we use to help customers across industries address their document processing challenges. Automated document processing workflows accurately extract structured information from documents, reducing manual effort....
Amazon SageMaker HyperPod enhances ML infrastructu...
In this post, we introduced three features in SageMaker HyperPod that enhance scalability and customizability for ML infrastructure. Continuous provisioning offers flexible resource provisioning to help you start training and deploying your models faster and manage your cluster more...
Fine-tune OpenAI GPT-OSS models using Amazon SageM...
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed...
Inline code nodes now supported in Amazon Bedrock ...
We are excited to announce the public preview of support for inline code nodes in Amazon Bedrock Flows. With this powerful new capability, you can write Python scripts directly within your workflow, alleviating the need for separate AWS Lambda...
Accelerate enterprise AI implementations with Amaz...
Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can...
Speed up delivery of ML workloads using Code Edito...
In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities...
How Infosys Topaz leverages Amazon Bedrock to tran...
In this blog, we examine the use case of a large energy supplier whose technical help desk agents answer customer calls and support field agents. We use Amazon Bedrock along with capabilities from Infosys Topaz™ to build a generative...
Create personalized products and marketing campaig...
Built using Amazon Nova in Amazon Bedrock, The Fragrance Lab represents a comprehensive end-to-end application that illustrates the transformative power of generative AI in retail, consumer goods, advertising, and marketing. In this post, we explore the development of The Fragrance...