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Enabling digital transformation to advance healthcare
Learn how Amazon Web Services and third-party solutions from AWS Marketplace equip healthcare organizations with tools that enhance data security, accelerate AI adoption, streamline workflows, and improve patient outcomes.
Introducing AWS Builder Center: A new home for the AWS builder community
Visit builder.aws.com to begin exploring AWS Builder Center. Sign up for a Builder ID if you don’t have one yet and claim your unique alias to access all features, including content creation, wishlist, and community engagement tools.
New Amazon EC2 P6e-GB200 UltraServers accelerated by NVIDIA Grace Blackwell GPUs for the highest AI performance
Amazon announces the general availability of EC2 P6e-GB200 UltraServers, powered by NVIDIA Grace Blackwell GB200 superchips that enable up to 72 GPUs with 360 petaflops of computing power for AI training and inference at the trillion-parameter scale.
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Darren Roback, Jeremy Cianella, 07/11/2025In an era of increasing digital transformation, electric utility providers are facing unprecedented challenges in meeting customer expectations for quick, efficient, and accessible service. Utility contact centers in particular face unique challenges in delivering effective customer service. Traditional Interactive Voice Response (IVR) systems can effectively handle direct, well-defined requests through touch-tone or basic voice commands, [...]
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Nick Aldridge, James Ward, Clare Liguori, 07/10/2025Developers are architecting and building systems of AI agents that work together to autonomously accomplish users’ tasks. In Part 1 of our blog series on Open Protocols for Agent Interoperability we covered how Model Context Protocol (MCP) can be used to facilitate inter-agent communication and the MCP specification enhancements AWS is working on to enable [...]
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Akira Mikami, Mitsuhiko Nishida, Junpei Ozono, 07/10/2025In this post, we explore how Furuno Electric built a comprehensive data management foundation using Amazon DataZone and other AWS services to transform from a traditional manufacturing company to a data-driven business.
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Jeremy Spell, Jeff DeMuth, 07/10/2025In this post, we review how to set up Redshift Serverless to use geospatial data contained within a data lake to enhance maps in ArcGIS Pro. This technique helps builders and GIS analysts use available datasets in data lakes and transform it in Amazon Redshift to further enrich the data before presenting it on a map.
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How to securely deliver business intelligence to internal-facing applications with Amazon QuickSightDodd Pfeffer, Deepak Singh, 07/10/2025In this post, we explore how to implement authentication and authorization requirements for Amazon QuickSight embedded web applications, focusing on various approaches for business intelligence delivery. We specifically address the use case of an enterprise with a central access management process, showing how QuickSight can be securely integrated into internal-facing applications.
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Ankur Mehrotra, 07/10/2025In this post, we share some of the new innovations in SageMaker AI that can accelerate how you build and train AI models. These innovations include new observability capabilities in SageMaker HyperPod, the ability to deploy JumpStart models on HyperPod, remote connections to SageMaker AI from local development environments, and fully managed MLflow 3.0.
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Tomonori Shimomura, Aman Shanbhag, Eric Saleh, Gopi Sekar, Matthew Nightingale, Piyush Kadam, Bhaskar Pratap, 07/10/2025With a one-click installation of the Amazon Elastic Kubernetes Service (Amazon EKS) add-on for SageMaker HyperPod observability, you can consolidate health and performance data from NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. In this post, we walk you through installing and using the unified dashboards of the out-of-the-box observability feature in SageMaker HyperPod. We cover the one-click installation from the Amazon SageMaker AI console, navigating the dashboard and metrics it consolidates, and advanced topics such as setting up custom alerts.
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Ram Vittal, Amit Modi, Rahul Easwar, Sandeep Raveesh-Babu, 07/10/2025In this post, we explore how Amazon SageMaker now offers fully managed support for MLflow 3.0, streamlining AI experimentation and accelerating your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.
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Vivek Gangasani, Andrew Smith, Chaitanya Hazarey, Piyush Daftary, Kareem Syed-Mohammed, 07/10/2025In this post, we announce Amazon SageMaker HyperPod support for deploying foundation models from SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. This new capability allows customers to train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model lifecycle.
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Durga Sury, Raj Bagwe, Sri Aakash Mandavilli, Edward Sun, 07/10/2025AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker Studio. In this post, we show you how to remotely connect your local VS Code to SageMaker Studio development environments to use your customized development environment while accessing Amazon SageMaker AI compute resources.