OpenSearch has introduced Star-Tree Index, a new feature that significantly improves aggregation performance for high-cardinality and multi-dimensional queries. This index pre-aggregates data across configured dimensions and metrics at ingestion time, enabling sub-second response times for frequent aggregations like terms, histogram, and range.
Star-Tree Index is designed for real-time analytics and requires no changes to query syntax; OpenSearch automatically uses the optimized path when supported queries are detected. Early benchmarks show faster aggregation performance on large datasets. This makes it ideal for use cases such as observability, personalization, and time-series dashboards. It works best with append-only data and builds during segment refresh/merge, with minimal impact on ingestion throughput.
Star-Tree Index is available in all regions where OpenSearch 3.1 is supported. The feature is opt-in and can be enabled at index creation time using composite index settings.
Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about Star-Tree Index, see the OpenSearch Documentation
OpenSearch has introduced Star-Tree Index, a new feature that significantly improves aggregation performance for high-cardinality and multi-dimensional queries. This index pre-aggregates data across configured dimensions and metrics at ingestion time, enabling sub-second response times for frequent aggregations like terms, histogram, and range. Star-Tree Index is designed for real-time analytics and requires no changes to query syntax; OpenSearch automatically uses the optimized path when supported queries are detected. Early benchmarks show faster aggregation performance on large datasets. This makes it ideal for use cases such as observability, personalization, and time-series dashboards. It works best with append-only data and builds during segment refresh/merge, with minimal impact on ingestion throughput. Star-Tree Index is available in all regions where OpenSearch 3.1 is supported. The feature is opt-in and can be enabled at index creation time using composite index settings. Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about Star-Tree Index, see the OpenSearch Documentation
Amazon OpenSearch Service introduces support for Derived Source, a new feature that can help reduce the amount of storage required for your OpenSearch Service domains. With derived source support, you can skip storing source fields and dynamically derive them when required.
OpenSearch stores each ingested document in the _source field and also indexes individual fields for search. The _source field can consume significant storage space. To reduce storage use, you can configure OpenSearch to skip storing the _source field and instead reconstruct it dynamically when needed, for example, during search, get, mget, reindex, or update operations.
Derived Source is available in all regions where OpenSearch 3.1 is supported. The feature is opt-in and can be enabled at index creation using composite index settings.
Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about Derived Source, see the OpenSearch documentation.
Amazon OpenSearch Service introduces support for Derived Source, a new feature that can help reduce the amount of storage required for your OpenSearch Service domains. With derived source support, you can skip storing source fields and dynamically derive them when required. OpenSearch stores each ingested document in the _source field and also indexes individual fields for search. The _source field can consume significant storage space. To reduce storage use, you can configure OpenSearch to skip storing the _source field and instead reconstruct it dynamically when needed, for example, during search, get, mget, reindex, or update operations. Derived Source is available in all regions where OpenSearch 3.1 is supported. The feature is opt-in and can be enabled at index creation using composite index settings. Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about Derived Source, see the OpenSearch documentation.
Amazon S3 Batch Operations now supports managing objects within an S3 bucket, prefix, suffix, or more, in a single step in AWS Management Console. When creating an S3 Batch Operation, customers can specify the objects on which to perform the operation. With this feature you have the option to instead specify an entire bucket, prefix, suffix, creation date, or storage class. Amazon S3 Batch Operations will then quickly apply the operation to all the matching objects and notify you when the job completes.
S3 Batch Operations lets you easily perform one-time or recurring batch workloads such as copying objects between staging and production buckets, restoring archived backups from S3 Glacier storage classes, or computing objects checksum to verify the content of stored datasets, at any scale. After starting your job, S3 Batch Operations automatically processes all of the objects that match your filtering criteria. You will receive a detailed completion report with the status of each object once the job completes.
This feature of S3 Batch Operations is available in all AWS Regions. You can get started through AWS Management Console, AWS Command Line Interface (CLI), or the AWS Software Development Kit (SDK) client. For pricing information, please visit the Management & Insights tab of the Amazon S3 pricing page. To learn more about S3 Batch Operations, visit the S3 User Guide.
Amazon S3 Batch Operations now supports managing objects within an S3 bucket, prefix, suffix, or more, in a single step in AWS Management Console. When creating an S3 Batch Operation, customers can specify the objects on which to perform the operation. With this feature you have the option to instead specify an entire bucket, prefix, suffix, creation date, or storage class. Amazon S3 Batch Operations will then quickly apply the operation to all the matching objects and notify you when the job completes.
S3 Batch Operations lets you easily perform one-time or recurring batch workloads such as copying objects between staging and production buckets, restoring archived backups from S3 Glacier storage classes, or computing objects checksum to verify the content of stored datasets, at any scale. After starting your job, S3 Batch Operations automatically processes all of the objects that match your filtering criteria. You will receive a detailed completion report with the status of each object once the job completes. This feature of S3 Batch Operations is available in all AWS Regions. You can get started through AWS Management Console, AWS Command Line Interface (CLI), or the AWS Software Development Kit (SDK) client. For pricing information, please visit the Management & Insights tab of the Amazon S3 pricing page. To learn more about S3 Batch Operations, visit the S3 User Guide.
Today, AWS announces the general availability of the new Amazon Elastic Compute Cloud (Amazon EC2) R8gn instances. These instances are powered by AWS Graviton4 processors to deliver up to 30% better compute performance than AWS Graviton3 processors. R8gn instances feature the latest 6th generation AWS Nitro Cards, and offer up to 600 Gbps network bandwidth, the highest network bandwidth among network optimized EC2 instances.
Take advantage of the enhanced networking capabilities of R8gn to scale the performance and throughput of network-intensive workloads such as SQL and NoSQL databases, and in-memory databases. For increased scalability, these instances offer instance sizes up to 48xlarge, including two metal sizes, up to 1,536 GiB of memory, and up to 60 Gbps of bandwidth to Amazon Elastic Block Store (EBS). These instances support Elastic Fabric Adapter (EFA) networking on the 16xlarge, 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes, which enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.
The new instances are available in the following AWS Regions: US East (N. Virginia), and US West (Oregon). Metal sizes are only available in US East (N. Virginia).
Today, AWS announces the general availability of the new Amazon Elastic Compute Cloud (Amazon EC2) R8gn instances. These instances are powered by AWS Graviton4 processors to deliver up to 30% better compute performance than AWS Graviton3 processors. R8gn instances feature the latest 6th generation AWS Nitro Cards, and offer up to 600 Gbps network bandwidth, the highest network bandwidth among network optimized EC2 instances. Take advantage of the enhanced networking capabilities of R8gn to scale the performance and throughput of network-intensive workloads such as SQL and NoSQL databases, and in-memory databases. For increased scalability, these instances offer instance sizes up to 48xlarge, including two metal sizes, up to 1,536 GiB of memory, and up to 60 Gbps of bandwidth to Amazon Elastic Block Store (EBS). These instances support Elastic Fabric Adapter (EFA) networking on the 16xlarge, 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes, which enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters. The new instances are available in the following AWS Regions: US East (N. Virginia), and US West (Oregon). Metal sizes are only available in US East (N. Virginia). To learn more, see Amazon R8gn Instances. To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs.
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.
