Amazon Managed Service for Prometheus is now available in Asia Pacific (Jakarta), Asia Pacific (Hyderabad), Asia Pacific (Osaka), Asia Pacific (Melbourne), Asia Pacific (Taipei), Canada West (Calgary), Europe (Spain), Israel (Tel Aviv), Mexico (Central), Middle East (Bahrain), and US West (N. California). Amazon Managed Service for Prometheus is a fully managed Prometheus-compatible monitoring service that makes it easy to monitor and alarm on operational metrics at scale.
The list of all supported regions where Amazon Managed Service for Prometheus is generally available can be found in the user guide. Customers can send up to 1 billion active metrics to a single workspace and can create multiple workspaces per account, where a workspace is a logical space dedicated to the storage and querying of Prometheus metrics.
To learn more about Amazon Managed Service for Prometheus, visit the user guide or product page.
Amazon Managed Service for Prometheus is now available in Asia Pacific (Jakarta), Asia Pacific (Hyderabad), Asia Pacific (Osaka), Asia Pacific (Melbourne), Asia Pacific (Taipei), Canada West (Calgary), Europe (Spain), Israel (Tel Aviv), Mexico (Central), Middle East (Bahrain), and US West (N. California). Amazon Managed Service for Prometheus is a fully managed Prometheus-compatible monitoring service that makes it easy to monitor and alarm on operational metrics at scale.
The list of all supported regions where Amazon Managed Service for Prometheus is generally available can be found in the user guide. Customers can send up to 1 billion active metrics to a single workspace and can create multiple workspaces per account, where a workspace is a logical space dedicated to the storage and querying of Prometheus metrics.
To learn more about Amazon Managed Service for Prometheus, visit the user guide or product page.
Starting today, customers can use the on-demand deployment option in Amazon Bedrock for their Meta Llama 3.3 models that have been fine-tuned or distilled in Bedrock. Models customized on or after September 15, 2025 will be eligible.
This enables Bedrock customers to reduce costs by processing requests in real time without requiring pre-provisioned compute resources. Customers only pay for what they use, eliminating the need for an always-on infrastructure.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in.
Starting today, customers can use the on-demand deployment option in Amazon Bedrock for their Meta Llama 3.3 models that have been fine-tuned or distilled in Bedrock. Models customized on or after September 15, 2025 will be eligible. This enables Bedrock customers to reduce costs by processing requests in real time without requiring pre-provisioned compute resources. Customers only pay for what they use, eliminating the need for an always-on infrastructure. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in. To get started, visit documentation here.
AWS Organizations provides a new State field in the AWS Organizations Console and APIs (DescribeAccount, ListAccounts, and ListAccountsForParent) to enhance AWS account lifecycle visibility. With this launch, the account state, a new State field replaced the existing account status, Status field in the AWS Organizations Console, however both Status and State fields will remain available in the APIs until September 9, 2026.
This launch allows you to have a more granular account state information such as, ‘SUSPENDED’ for AWS-enforced suspension, ‘PENDING_CLOSURE’ for in-process closure requests, and ‘CLOSED’ for accounts in their 90-day reinstatement window, and more. After September, 2026 the Status field will be fully deprecated. Customers using account vending pipelines should update their implementations to reference the State field before the Status field deprecation date. This feature is available in all AWS commercial and AWS GovCloud (US) Regions. To get started managing your accounts, please see the blog post and documentation.
AWS Organizations provides a new State field in the AWS Organizations Console and APIs (DescribeAccount, ListAccounts, and ListAccountsForParent) to enhance AWS account lifecycle visibility. With this launch, the account state, a new State field replaced the existing account status, Status field in the AWS Organizations Console, however both Status and State fields will remain available in the APIs until September 9, 2026. This launch allows you to have a more granular account state information such as, ‘SUSPENDED’ for AWS-enforced suspension, ‘PENDING_CLOSURE’ for in-process closure requests, and ‘CLOSED’ for accounts in their 90-day reinstatement window, and more. After September, 2026 the Status field will be fully deprecated. Customers using account vending pipelines should update their implementations to reference the State field before the Status field deprecation date. This feature is available in all AWS commercial and AWS GovCloud (US) Regions. To get started managing your accounts, please see the blog post and documentation.
Today, Amazon SageMaker HyperPod announces the general availability of the health monitoring agent for Slurm clusters. SageMaker HyperPod helps you provision resilient clusters for running machine learning (ML) workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs). The health monitoring agent performs passive, background health checks of instances to identify problems in key areas without impact on application behavior or performance, flags failures instantly, and replaces any unhealthy instances to keep your training jobs running smoothly.
The agent runs continuously on all GPU- or Trainium-based nodes in your HyperPod cluster, watching for hardware issues such as unresponsive GPUs or NVLink error counters. When a fault is detected, it marks the node as unhealthy and automatically reboots or replaces it with a healthy node, keeping your jobs running without requiring manual intervention. The agent also follows a co-ordinated approach to handling failures with the job auto-resume functionality available with Slurm clusters. For example, jobs with auto-resume enabled will continue from the last saved checkpoint once nodes are replaced by the agent. This hands-free recovery—already available on HyperPod clusters orchestrated with Amazon EKS—now gives Slurm clusters the same resilient environment, helping teams train large models for weeks without disruption and reclaim time and costs that would otherwise be lost to mid-run failures. In addition, customers can now also reboot their nodes using a simple command in case of intermittent issues such as GPU driver issues requiring reset.
Health monitoring agent for Slurm is available in all regions where HyperPod is generally available. The agent is auto-enabled on all newly created Slurm clusters; to enable it on an existing cluster, simply upgrade to the latest HyperPod AMI by calling the UpdateClusterSoftware API. To learn more, visit the Amazon SageMaker HyperPod documentation.
Today, Amazon SageMaker HyperPod announces the general availability of the health monitoring agent for Slurm clusters. SageMaker HyperPod helps you provision resilient clusters for running machine learning (ML) workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs). The health monitoring agent performs passive, background health checks of instances to identify problems in key areas without impact on application behavior or performance, flags failures instantly, and replaces any unhealthy instances to keep your training jobs running smoothly.
The agent runs continuously on all GPU- or Trainium-based nodes in your HyperPod cluster, watching for hardware issues such as unresponsive GPUs or NVLink error counters. When a fault is detected, it marks the node as unhealthy and automatically reboots or replaces it with a healthy node, keeping your jobs running without requiring manual intervention. The agent also follows a co-ordinated approach to handling failures with the job auto-resume functionality available with Slurm clusters. For example, jobs with auto-resume enabled will continue from the last saved checkpoint once nodes are replaced by the agent. This hands-free recovery—already available on HyperPod clusters orchestrated with Amazon EKS—now gives Slurm clusters the same resilient environment, helping teams train large models for weeks without disruption and reclaim time and costs that would otherwise be lost to mid-run failures. In addition, customers can now also reboot their nodes using a simple command in case of intermittent issues such as GPU driver issues requiring reset.
Health monitoring agent for Slurm is available in all regions where HyperPod is generally available. The agent is auto-enabled on all newly created Slurm clusters; to enable it on an existing cluster, simply upgrade to the latest HyperPod AMI by calling the UpdateClusterSoftware API. To learn more, visit the Amazon SageMaker HyperPod documentation.
Amazon Connect Cases now supports filtering by date ranges in the case list view, enabling contact center managers and agents to efficiently manage their case workloads. For example, users can filter cases created in the last 30 days for monthly reporting, view cases modified in the last 24 hours to monitor recent activity, or surface cases with potential SLA breaches in the next 2 days to help prevent violations.
Amazon Connect Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town) AWS regions. To learn more and get started, visit the Amazon Connect Cases webpage and documentation.
Amazon Connect Cases now supports filtering by date ranges in the case list view, enabling contact center managers and agents to efficiently manage their case workloads. For example, users can filter cases created in the last 30 days for monthly reporting, view cases modified in the last 24 hours to monitor recent activity, or surface cases with potential SLA breaches in the next 2 days to help prevent violations. Amazon Connect Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town) AWS regions. To learn more and get started, visit the Amazon Connect Cases webpage and documentation.
En los últimos años, la IA se ha integrado a la perfección en el tejido de nuestras rutinas diarias, para transformar la manera en que accedemos a la información y organizamos nuestras vidas. Desde motores de búsqueda inteligentes hasta asistentes virtuales que nos ayudan a planificar viajes, la IA está detrás de la comodidad sin esfuerzo que ahora esperamos.
También transforma la manera en que compramos. Cada vez más, adoptamos herramientas de compra de IA que nos ayudan a encontrar productos más fácil. Pero eso es solo el comienzo de lo que puede hacer el comercio conversacional. Al igual que los consumidores lo quieren en la tienda, en línea buscan recomendaciones que reflejen su sentido del estilo personal.
Conozcan Ask Ralph, un nuevo compañero de estilo impulsado por IA que no solo ayuda con el descubrimiento de productos, sino que también inspira a los consumidores con la versión única e icónica del estilo de Ralph Lauren.
Pregunten a Ralph: Un compañero de estilo impulsado por IA
Ask Ralph es una experiencia de compra de IA conversacional basada en Azure OpenAI y disponible en la aplicación Ralph Lauren en EE. UU. Pueden interactuar con Ask Ralph como lo harían con un estilista en una tienda Ralph Lauren con preguntas simples y conversacionales o por medio de prompts para encontrar el look perfecto para cualquier ocasión.
Ya sea que busquen renovar su guardarropa para el otoño o se preguntan qué ponerse para un concierto en el parque, Ask Ralph responde con atuendos seleccionados, estilizados por completo, exhibidos y comprables a nivel visual de toda la marca Polo Ralph Lauren, adaptados a sus prompts únicos.
El deleite del comercio conversacional
Ask Ralph es parte de un movimiento más amplio, uno en el que la IA no solo ayuda, sino que inspira.
Por medio del lenguaje natural, Ask Ralph interpreta prompts abiertos, hace preguntas aclaratorias y ofrece recomendaciones de atuendos visualizadas de manera hermosa, que se adaptan a su consulta, todo basado en el inventario disponible en tiempo real de Ralph Lauren.
Construido para el futuro, basado en el legado
Durante casi 60 años, Ralph Lauren ha sido pionero en la creación de experiencias minoristas cinematográficas y de transporte. Hace veinticinco años, Microsoft y Ralph Lauren se unieron para lanzar una de las primeras plataformas de comercio electrónico de la moda, estableciendo un estándar de la industria, y ahora, juntos, redefinimos de nuevo la experiencia de compra con Ask Ralph.
Como Naveen Seshadri, director digital de Ralph Lauren, compartió en una entrevista reciente: «En Ralph Lauren, nuestro enfoque siempre está en el consumidor. Aprovechamos tecnologías innovadoras para crear una experiencia elevada y personalizada que atraiga a los clientes al mundo icónico de Ralph en cada interacción. El lanzamiento de Ask Ralph es una continuación de ese compromiso».
Para escuchar más de Naveen sobre la visión detrás de Ask Ralph, vean el video del cliente de Ralph Lauren.
IA agéntica: la nueva frontera
Ask Ralph está impulsado con las funcionalidades de inteligencia artificial agéntica de Azure: sistemas inteligentes que planifican, razonan y actúan. Estos agentes transforman el comercio minorista al permitir experiencias inmersivas y personalizadas a escala.
«En Ralph Lauren, nuestro enfoque siempre está en el consumidor. Aprovechamos tecnologías innovadoras para crear una experiencia elevada y personalizada que atraiga a los clientes al mundo icónico de Ralph en cada interacción. El lanzamiento de Ask Ralph es una continuación de ese compromiso».
—Naveen Seshadri, director digital de Ralph Lauren
Confianza, creatividad, conexión
En el fondo, Ask Ralph trata sobre la inspiración. Se trata de ayudar a las personas a encontrar nuevas formas de expresar su estilo personal.
Este es solo el comienzo para Ask Ralph, que continuará su evolución con nuevas funciones y ofertas para ofrecer una experiencia aún más personalizada, así como expandirse a través de mercados, plataformas y marcas adicionales de Ralph Lauren.
¿Listos para transformar la experiencia de compra con IA?
Con Azure AI, los minoristas tienen el poder de crear experiencias de compra inmersivas e inteligentes que escalan, se adaptan e inspiran. Ya sea que busquen personalizar los recorridos de los clientes, optimizar el inventario o capacitar a su fuerza laboral, la plataforma de inteligencia artificial de Microsoft está lista para ayudarlos a innovar con confianza.
Únanse a nosotros en un taller de AI.deation para explorar cómo la IA agéntica puede elevar su negocio, desde el concepto hasta la producción. Creemos juntos el futuro del comercio minorista, una conversación a la vez.
You can now run OpenSearch version 3.1 in Amazon OpenSearch Service. OpenSearch 3.1 introduces several improvements in areas like search relevance and performance, and introduces features that simplify development of vector-driven applications for generative AI workloads.
This launch incorporates Lucene 10 that enables optimized vector field indexing resulting in faster indexing times and reduced index sizes, sparse indexing for CPU and storage efficiency improvements, and vector quantization to reduce memory usage. Other key areas of improvement include improved range query performance, which benefits log analytics and time-series workloads, and reduced latency for high-cardinality aggregations.
This launch also introduces a new Search Relevance Workbench, which provides integrated tools for teams to evaluate and optimize search quality through experimentation. Additionally, this launch includes several improvements in vector search capabilities. First, Z-score normalization improves hybrid search reliability by reducing the impact of outliers and different score scales. Finally, you can now boost efficiency of searches using memory-optimized search that enables the Faiss engine to operate efficiently by memory-mapping the index file and using the operating system’s file cache to serve search requests.
For information on upgrading to OpenSearch 3.1, please see the documentation. OpenSearch 3.1 is now available in all AWS Regions where Amazon OpenSearch Service is available.
You can now run OpenSearch version 3.1 in Amazon OpenSearch Service. OpenSearch 3.1 introduces several improvements in areas like search relevance and performance, and introduces features that simplify development of vector-driven applications for generative AI workloads.
This launch incorporates Lucene 10 that enables optimized vector field indexing resulting in faster indexing times and reduced index sizes, sparse indexing for CPU and storage efficiency improvements, and vector quantization to reduce memory usage. Other key areas of improvement include improved range query performance, which benefits log analytics and time-series workloads, and reduced latency for high-cardinality aggregations.
This launch also introduces a new Search Relevance Workbench, which provides integrated tools for teams to evaluate and optimize search quality through experimentation. Additionally, this launch includes several improvements in vector search capabilities. First, Z-score normalization improves hybrid search reliability by reducing the impact of outliers and different score scales. Finally, you can now boost efficiency of searches using memory-optimized search that enables the Faiss engine to operate efficiently by memory-mapping the index file and using the operating system’s file cache to serve search requests.
For information on upgrading to OpenSearch 3.1, please see the documentation. OpenSearch 3.1 is now available in all AWS Regions where Amazon OpenSearch Service is available.
We are pleased to announce general availability of Amazon EC2 P6-B200 instances on SageMaker notebooks.
Amazon EC2 P6-B200 instances are powered by 8 NVIDIA Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and 5th Generation Intel Xeon processors (Emerald Rapids). These instances deliver up to 2x better performance compared to P5en instances for AI training. Customers can use P6-B200 instances to interactively develop and fine-tune large foundation models, including LLMs, mixture of experts models, and multi-modal reasoning models. These instances enable efficient experimentation with larger models directly in JupyterLab or CodeEditor environments for generative AI applications such as enterprise copilots and content generation across text, images, and video.
Amazon EC2 P6-B200 instances are available for SageMaker notebooks in the AWS US East (Ohio) and US West (Oregon) regions.
We are pleased to announce general availability of Amazon EC2 P6-B200 instances on SageMaker notebooks. Amazon EC2 P6-B200 instances are powered by 8 NVIDIA Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and 5th Generation Intel Xeon processors (Emerald Rapids). These instances deliver up to 2x better performance compared to P5en instances for AI training. Customers can use P6-B200 instances to interactively develop and fine-tune large foundation models, including LLMs, mixture of experts models, and multi-modal reasoning models. These instances enable efficient experimentation with larger models directly in JupyterLab or CodeEditor environments for generative AI applications such as enterprise copilots and content generation across text, images, and video. Amazon EC2 P6-B200 instances are available for SageMaker notebooks in the AWS US East (Ohio) and US West (Oregon) regions. Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.
Amazon Relational Database Service (RDS) Proxy now supports end-to-end IAM authentication for connections to Amazon Aurora and RDS database instances. This feature allows you to connect from your applications to your databases through RDS Proxy using AWS Identity and Access Management (IAM) authentication. End-to-end IAM authentication simplifies credential management, reduces credential rotation overhead, and enables you to leverage IAM’s robust authentication and authorization capabilities throughout your database connection path.
With end-to-end IAM authentication, you can now connect to your databases through RDS Proxy without needing to register or store credentials in Secrets Manager. End-to-end IAM authentication is available for MySQL and PostgreSQL database engines in all AWS Regions where RDS Proxy is supported.
Many applications, including those built on modern serverless architectures, may need to have a high number of open connections to the database or may frequently open and close database connections, exhausting the database memory and compute resources. Amazon RDS Proxy allows applications to pool and share database connections, improving your database efficiency and application scalability. RDS Proxy helps improve application scalability, resiliency, and security.
For information on supported database engine versions and regional availability of RDS Proxy, refer to our RDS and Aurora documentations.
Amazon Relational Database Service (RDS) Proxy now supports end-to-end IAM authentication for connections to Amazon Aurora and RDS database instances. This feature allows you to connect from your applications to your databases through RDS Proxy using AWS Identity and Access Management (IAM) authentication. End-to-end IAM authentication simplifies credential management, reduces credential rotation overhead, and enables you to leverage IAM’s robust authentication and authorization capabilities throughout your database connection path. With end-to-end IAM authentication, you can now connect to your databases through RDS Proxy without needing to register or store credentials in Secrets Manager. End-to-end IAM authentication is available for MySQL and PostgreSQL database engines in all AWS Regions where RDS Proxy is supported. Many applications, including those built on modern serverless architectures, may need to have a high number of open connections to the database or may frequently open and close database connections, exhausting the database memory and compute resources. Amazon RDS Proxy allows applications to pool and share database connections, improving your database efficiency and application scalability. RDS Proxy helps improve application scalability, resiliency, and security. For information on supported database engine versions and regional availability of RDS Proxy, refer to our RDS and Aurora documentations.
Amazon ECS Service Connect now supports seamless communication between services residing in different AWS accounts through integration with AWS Resource Access Manager (AWS RAM). This enhancement simplifies resource sharing, reduces duplication, and promotes consistent service-to-service communication across environments for organizations with multi-account architectures.
Amazon ECS Service Connect leverages AWS Cloud Map namespaces for storing information about ECS services and tasks. To enable seamless cross-account communication between Amazon ECS Service Connect services, you can now share the underlying AWS Cloud Map namespaces using AWS RAM with individual AWS accounts, specific Organizational Units (OUs), or your entire AWS Organization. To get started, create a resource share in AWS RAM, add the namespaces you want to share, and specify the principals (accounts, OUs, or the organization) that should have access. This enables platform engineers to use the same namespace to register Amazon ECS Service Connect services residing in multiple AWS accounts, simplifying service discovery and connectivity. Application developers can then build services that rely on a consistent, shared registry without worrying about availability or synchronization across accounts. Cross-account connectivity support improves operational efficiency and makes it easier to scale Amazon ECS workloads as your organization grows by reducing duplication and streamlining access to common services.
This feature is available with both Fargate and EC2 launch modes now in all commercial AWS Regions via the AWS Management Console, API, SDK, CLI, and CloudFormation. To learn more, please refer to the Amazon ECS Service Connect documentation.
Amazon ECS Service Connect now supports seamless communication between services residing in different AWS accounts through integration with AWS Resource Access Manager (AWS RAM). This enhancement simplifies resource sharing, reduces duplication, and promotes consistent service-to-service communication across environments for organizations with multi-account architectures. Amazon ECS Service Connect leverages AWS Cloud Map namespaces for storing information about ECS services and tasks. To enable seamless cross-account communication between Amazon ECS Service Connect services, you can now share the underlying AWS Cloud Map namespaces using AWS RAM with individual AWS accounts, specific Organizational Units (OUs), or your entire AWS Organization. To get started, create a resource share in AWS RAM, add the namespaces you want to share, and specify the principals (accounts, OUs, or the organization) that should have access. This enables platform engineers to use the same namespace to register Amazon ECS Service Connect services residing in multiple AWS accounts, simplifying service discovery and connectivity. Application developers can then build services that rely on a consistent, shared registry without worrying about availability or synchronization across accounts. Cross-account connectivity support improves operational efficiency and makes it easier to scale Amazon ECS workloads as your organization grows by reducing duplication and streamlining access to common services.
This feature is available with both Fargate and EC2 launch modes now in all commercial AWS Regions via the AWS Management Console, API, SDK, CLI, and CloudFormation. To learn more, please refer to the Amazon ECS Service Connect documentation.
