AWS Directory Service for Microsoft Active Directory (AWS Managed Microsoft AD) now offers certificate auto-enrollment for LDAPS and Smart Card and certificate based authentication with AWS Private Certificate Authority (AWS Private CA) through AWS Private CA Connector for AD. This integration enables automatic issuance, renewal, and management of certificates to AWS Managed Microsoft AD domain controllers, eliminating the need to maintain certificate authorities on Amazon EC2 instances.
By leveraging this fully managed solution, you can reduce costs of operating certificate authority infrastructure for Active Directory and simplify certificate management with AWS Private CA’s highly available, HSM-backed infrastructure. The integration supports LDAPS and smart card authentication while providing automatic certificate lifecycle management, flexible certificate control, and built-in security capabilities that streamline migration of Active Directory-aware workloads to AWS.
This feature is available in all AWS Regions where AWS Private CA Connector for AD is offered.
AWS Directory Service for Microsoft Active Directory (AWS Managed Microsoft AD) now offers certificate auto-enrollment for LDAPS and Smart Card and certificate based authentication with AWS Private Certificate Authority (AWS Private CA) through AWS Private CA Connector for AD. This integration enables automatic issuance, renewal, and management of certificates to AWS Managed Microsoft AD domain controllers, eliminating the need to maintain certificate authorities on Amazon EC2 instances. By leveraging this fully managed solution, you can reduce costs of operating certificate authority infrastructure for Active Directory and simplify certificate management with AWS Private CA’s highly available, HSM-backed infrastructure. The integration supports LDAPS and smart card authentication while providing automatic certificate lifecycle management, flexible certificate control, and built-in security capabilities that streamline migration of Active Directory-aware workloads to AWS. This feature is available in all AWS Regions where AWS Private CA Connector for AD is offered. You can easily set up AWS Private CA integration with your directory in just a few clicks or programmatically via API. To get started, follow the step-by-step instructions in the Set up AWS Private CA Connector for AD for AWS Managed Microsoft AD documentation.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Asia Pacific (Osaka) and AWS Canada (Central) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.
AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Asia Pacific (Osaka) and AWS Canada (Central) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
Amazon ElastiCache now supports Graviton3-based M7g and R7g node families in the following AWS Regions: Canada (Calgary), Middle East & Africa (Bahrain, Cape Town, Dubai, Tel Aviv), Europe (Milan, Paris (introducing R7g), and Zurich), Asia Pacific (Hong Kong, Jakarta, Kuala Lumpur, Melbourne, and Osaka).
ElastiCache Graviton3 nodes deliver improved price-performance compared to Graviton2. As an example, when running ElastiCache for Redis OSS on an R7g.4xlarge node, you can achieve up to 28% increased throughput (read and write operations per second) and up to 21% improved P99 latency, compared to running on R6g.4xlarge. In addition, these nodes deliver up to 25% higher networking bandwidth.
For complete information on pricing and regional availability, please refer to the Amazon ElastiCache pricing page. To get started, create a new cluster or upgrade to Graviton3 using the AWS Management Console. For more information on supported node types, please refer to the documentation.
Amazon ElastiCache now supports Graviton3-based M7g and R7g node families in the following AWS Regions: Canada (Calgary), Middle East & Africa (Bahrain, Cape Town, Dubai, Tel Aviv), Europe (Milan, Paris (introducing R7g), and Zurich), Asia Pacific (Hong Kong, Jakarta, Kuala Lumpur, Melbourne, and Osaka). ElastiCache Graviton3 nodes deliver improved price-performance compared to Graviton2. As an example, when running ElastiCache for Redis OSS on an R7g.4xlarge node, you can achieve up to 28% increased throughput (read and write operations per second) and up to 21% improved P99 latency, compared to running on R6g.4xlarge. In addition, these nodes deliver up to 25% higher networking bandwidth. For complete information on pricing and regional availability, please refer to the Amazon ElastiCache pricing page. To get started, create a new cluster or upgrade to Graviton3 using the AWS Management Console. For more information on supported node types, please refer to the documentation.
Ralph Lauren presenta Ask Ralph, una nueva experiencia de compra conversacional con IA
Con la tecnología de Microsoft Azure OpenAI, la nueva función de la aplicación Ralph Lauren pone la visión icónica de la marca sobre el estilo en manos de los consumidores
NUEVA YORK – Ralph Lauren (NYSE:RL) presentó Ask Ralph, una nueva experiencia de compra conversacional impulsada por IA que invita a los consumidores a interactuar e inspirarse en la visión única e icónica del estilo de Ralph Lauren. A través de la combinación de tecnología avanzada de inteligencia artificial con la marca atemporal de Ralph Lauren, Ask Ralph proporciona inspiración de estilo al ofrecer múltiples presentaciones visuales de atuendos completos, personalizados según los prompts del usuario, de todo el inventario disponible dentro de la marca Polo Ralph Lauren para hombres y mujeres. Ask Ralph comienza a implementarse hoy para los usuarios de la aplicación Ralph Lauren en los Estados Unidos.
Para continuar con una asociación de décadas, Ask Ralph se desarrolló con Microsoft en su plataforma Azure OpenAI y utiliza tecnología avanzada de IA conversacional y procesamiento de lenguaje natural para comprender prompts abiertos, interpretar el contexto y brindar recomendaciones personalizadas para imitar de cerca la experiencia de hablar con un estilista en la tienda.
«Hace veinticinco años, nos asociamos con Microsoft para lanzar una de las primeras plataformas de comercio electrónico de la industria de la moda, y hoy, una vez más, redefinimos la experiencia de compra para la próxima generación», dijo David Lauren, director de marca e innovación de Ralph Lauren Corporation. «Ya sea que se preparen para un primer día de un nuevo trabajo o busquen crear el look perfecto para salir por la noche, Ask Ralph es algo más que un descubrimiento: se trata de involucrar a los consumidores con lo que más les gusta de Ralph Lauren: nuestra visión icónica y única del estilo, que brinda looks atemporales de pies a cabeza que los inspiran a entrar en nuestro mundo».
Los compradores pueden interactuar con Ask Ralph tal como lo harían con un estilista en una tienda Ralph Lauren, a través de prompts simples y conversacionales. Desde «¿Qué debo ponerme para un concierto?» hasta «Muéstrame algunos suéteres Polo Bear para mujer», o consultas de estilo como: «¿Cómo puedo combinar mi blazer azul marino para hombre?» Ask Ralph responde, al mostrar de manera visual los looks completos de Polo Ralph Lauren con consejos de estilo, con la integración contenido de todos los canales digitales de Ralph Lauren. Los usuarios pueden hacer preguntas aclaratorias y refinar las recomendaciones para alinearlas con su propio sentido del estilo. Ask Ralph facilita la adición de elementos individuales de un look a los carritos de compras o la compra de la recomendación de pies a cabeza.
«La IA transforma la manera en que los consumidores se inspiran, educan y compran a las marcas de moda de todo el mundo», dijo Shelley Bransten, vicepresidenta corporativa de Soluciones Industriales Globales de Microsoft. «Estamos orgullosos de traer la combinación de nuestras capacidades de IA generativa de confianza a través de Azure OpenAI junto con la marca icónica de Ralph Lauren para allanar el camino para una experiencia de comercio conversacional nueva por completo».
Basada en la forma en que los usuarios en un inicio usan e interactúan con Ask Ralph, la herramienta continuará desarrollándose, incluido el lanzamiento de nuevas funciones y el aumento de las experiencias personalizadas, la expansión a marcas adicionales de Ralph Lauren y el lanzamiento en más plataformas en mercados de todo el mundo.
Ask Ralph es el último hito en la rica historia de innovación de Ralph Lauren y en liderar la industria en la creación de experiencias minoristas inmersivas y cinematográficas que transportan a los consumidores al mundo de Ralph Lauren. Hace veinticinco años, la compañía fue una de las primeras marcas de lujo en ser pionera en el comercio electrónico, para establecer un nuevo estándar de la industria. Siempre con la meta de superar los límites para sorprender, deleitar e inspirar a los consumidores, la compañía ha experimentado durante mucho tiempo con ideas de vanguardia que estaban años adelantadas a su tiempo, desde tecnología de compras interactivas y virtuales hasta hologramas, proyecciones 4D y animaciones CGI.
La compañía también continúa su inversión en inteligencia artificial y otras tecnologías para mejorar la experiencia del consumidor, como un marketing más personalizado y experiencias digitales atractivas, así como para optimizar sus operaciones, incluida la gestión predictiva de inventario y el pronóstico de la demanda de productos.
Las imágenes y los recursos de apoyo relacionados con el anuncio están disponibles en el kit de prensa.
ACERCA DE RALPH LAUREN CORPORATION
Ralph Lauren Corporation (NYSE:RL) es un líder mundial en el diseño, comercialización y distribución de productos de estilo de vida de lujo en cinco categorías: ropa, calzado y accesorios, hogar, fragancias y hospitalidad. Durante casi 60 años, Ralph Lauren ha buscado inspirar el sueño de una vida mejor a través de la autenticidad y el estilo atemporal. Su reputación e imagen distintiva se han desarrollado en una amplia gama de productos, marcas, canales de distribución y mercados internacionales. Las marcas de la compañía, que incluyen Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children y Chaps, entre otras, constituyen una de las familias de marcas de consumo más reconocidas del mundo. Para obtener más información, visiten corporate.ralphlauren.com.
ACERCA DE MICROSOFT Microsoft (Nasdaq «MSFT» @microsoft) crea plataformas y herramientas impulsadas por IA para ofrecer soluciones innovadoras que satisfagan las necesidades cambiantes de nuestros clientes. La empresa de tecnología se compromete a hacer que la IA esté disponible de manera amplia y hacerlo de manera responsable, con la misión de impulsar a todas las personas y organizaciones del planeta para lograr más.
Starting today, AWS WAF is available in the AWS Asia Pacific (Taipei) Region.
AWS WAF is a web application firewall that helps you protect your web application resources against common web exploits and bots that can affect availability, compromise security, or consume excessive resources.
To see the full list of regions where AWS WAF is currently available, visit the AWS Region Table. Please note that AWS WAF Bot Control with targeted level of inspection and Anti-DDoS managed rule group are not currently available in this region. For more information about the service, visit the AWS WAF page. For more information about pricing, visit the AWS WAF Pricing page.
Starting today, AWS WAF is available in the AWS Asia Pacific (Taipei) Region.
AWS WAF is a web application firewall that helps you protect your web application resources against common web exploits and bots that can affect availability, compromise security, or consume excessive resources.
To see the full list of regions where AWS WAF is currently available, visit the AWS Region Table. Please note that AWS WAF Bot Control with targeted level of inspection and Anti-DDoS managed rule group are not currently available in this region. For more information about the service, visit the AWS WAF page. For more information about pricing, visit the AWS WAF Pricing page.
Today, Amazon Web Service (AWS) announces the general availability of managed tiered checkpointing for Amazon SageMaker HyperPod, a new capability designed to reduce model recovery time and minimize loss in training progress. As AI training scales, the likelihood of infrastructure failures increases, making efficient checkpointing critical. Traditional checkpointing methods can be slow and resource-intensive, especially for large models. SageMaker HyperPod’s managed tiered checkpointing addresses this by using CPU memory to store frequent checkpoints for rapid recovery, while periodically persisting data to Amazon S3 for long-term durability. This hybrid approach minimizes training loss and significantly reduces the time to resume training after a failure.
With managed tiered checkpointing organizations can train reliably, with high throughput on large-scale clusters. The solution allows customers to configure checkpoint frequency and retention policies across both in-memory and persistent storage tiers. By storing frequently in memory customers can recover quickly while minimizing storage costs. Integrated with PyTorch’s Distributed Checkpoint (DCP), customers can easily implement checkpointing with only a few lines of code, while gaining the performance benefits of in-memory storage.
This feature is currently available for SageMaker HyperPod clusters using the EKS orchestrator. Customers can enable managed tiered checkpointing by specifying an API parameter when creating or updating a HyperPod cluster via the CreateCluster or UpdateCluster API. Customers can then use the sagemaker-checkpointing python library to implement managed tiered checkpointing with minimal code changes to their training scripts.
Managed tiered checkpointing is available in all regions where SageMaker HyperPod is currently available. To learn more, please refer to the blog post and documentation.
Today, Amazon Web Service (AWS) announces the general availability of managed tiered checkpointing for Amazon SageMaker HyperPod, a new capability designed to reduce model recovery time and minimize loss in training progress. As AI training scales, the likelihood of infrastructure failures increases, making efficient checkpointing critical. Traditional checkpointing methods can be slow and resource-intensive, especially for large models. SageMaker HyperPod’s managed tiered checkpointing addresses this by using CPU memory to store frequent checkpoints for rapid recovery, while periodically persisting data to Amazon S3 for long-term durability. This hybrid approach minimizes training loss and significantly reduces the time to resume training after a failure. With managed tiered checkpointing organizations can train reliably, with high throughput on large-scale clusters. The solution allows customers to configure checkpoint frequency and retention policies across both in-memory and persistent storage tiers. By storing frequently in memory customers can recover quickly while minimizing storage costs. Integrated with PyTorch’s Distributed Checkpoint (DCP), customers can easily implement checkpointing with only a few lines of code, while gaining the performance benefits of in-memory storage. This feature is currently available for SageMaker HyperPod clusters using the EKS orchestrator. Customers can enable managed tiered checkpointing by specifying an API parameter when creating or updating a HyperPod cluster via the CreateCluster or UpdateCluster API. Customers can then use the sagemaker-checkpointing python library to implement managed tiered checkpointing with minimal code changes to their training scripts. Managed tiered checkpointing is available in all regions where SageMaker HyperPod is currently available. To learn more, please refer to the blog post and documentation.
Today, AWS announced the general availability of Custom Blueprints, a new feature in Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature allows customers to use their own managed policies as per their corporate security requirements to create a project role in SageMaker Unified Studio. Customers can either replace the managed policies provide by Amazon SageMaker Unified Studio as part of the tooling blueprint with their custom policies or enrich the existing policies by appending additional policies.
In addition to allowing you to bring your own managed policies, Custom Blueprints is designed to provide you the ability to configure the infrastructure and resources that you want to deploy in the project created in Amazon SageMaker Unified Studio. Using your own AWS CloudFormation templates you can define and customize the parameters and configuration for any AWS resources such as Amazon EMR on EC2, AWS Glue Data Catalog, and Amazon Redshift. You can replace the service managed blueprints with your custom blueprints in order to ensure standardization across your entire organization. The sample templates to create your custom blueprints are available here.
The ability to use Custom Blueprint is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the supported regions list for more details. For instructions on how to get started, visit the Amazon SageMaker documentation.
Today, AWS announced the general availability of Custom Blueprints, a new feature in Amazon SageMaker Unified Studio, part of the next generation of Amazon SageMaker. This feature allows customers to use their own managed policies as per their corporate security requirements to create a project role in SageMaker Unified Studio. Customers can either replace the managed policies provide by Amazon SageMaker Unified Studio as part of the tooling blueprint with their custom policies or enrich the existing policies by appending additional policies. In addition to allowing you to bring your own managed policies, Custom Blueprints is designed to provide you the ability to configure the infrastructure and resources that you want to deploy in the project created in Amazon SageMaker Unified Studio. Using your own AWS CloudFormation templates you can define and customize the parameters and configuration for any AWS resources such as Amazon EMR on EC2, AWS Glue Data Catalog, and Amazon Redshift. You can replace the service managed blueprints with your custom blueprints in order to ensure standardization across your entire organization. The sample templates to create your custom blueprints are available here. The ability to use Custom Blueprint is available in all AWS Commercial Regions where the next generation of Amazon SageMaker is available. See the supported regions list for more details. For instructions on how to get started, visit the Amazon SageMaker documentation.
Today, we are announcing improvements to the Amazon Q Developer chat experience in Amazon SageMaker Unified Studio Jupyter notebooks and adding Amazon Q Developer in the command line in Jupyter notebooks and Code Editor. By integrating with Model Context Protocol (MCP) servers, Amazon Q Developer is aware of your SageMaker Unified Studio project resources, including data, compute, and code, and provides personalized assistance for data engineering and machine learning development work.
These new capabilities provide highly relevant responses to assist with tasks like code refactoring, file modification, and troubleshooting. This helps data scientists and data engineers quickly set up their integrated development environments and work more efficiently while maintaining transparency into how the AI assistant is acting on their behalf.
These features are available at no additional cost with the Amazon Q Developer Free Tier in all AWS Regions where Amazon SageMaker Unified Studio is available. To make even more use of these features, we recommend enabling Amazon Q Developer Pro. To do so, please refer to the documentation.
Today, we are announcing improvements to the Amazon Q Developer chat experience in Amazon SageMaker Unified Studio Jupyter notebooks and adding Amazon Q Developer in the command line in Jupyter notebooks and Code Editor. By integrating with Model Context Protocol (MCP) servers, Amazon Q Developer is aware of your SageMaker Unified Studio project resources, including data, compute, and code, and provides personalized assistance for data engineering and machine learning development work. These new capabilities provide highly relevant responses to assist with tasks like code refactoring, file modification, and troubleshooting. This helps data scientists and data engineers quickly set up their integrated development environments and work more efficiently while maintaining transparency into how the AI assistant is acting on their behalf. These features are available at no additional cost with the Amazon Q Developer Free Tier in all AWS Regions where Amazon SageMaker Unified Studio is available. To make even more use of these features, we recommend enabling Amazon Q Developer Pro. To do so, please refer to the documentation.
Amazon CloudFront expands its IPv6 capabilities by introducing support for IPv6 connectivity to origin servers, allowing customers to implement end-to-end IPv6 content delivery for their web applications. Support for IPv6 origins enables customers to send IPv6 traffic all the way to their origins, allowing them to meet their architectural and regulatory requirements for IPv6 adoption. End-to-end IPv6 support improves network performance for end users connecting over IPv6 networks, and also removes concerns for IPv4 address exhaustion for origin infrastructure.
Previously, CloudFront only supported IPv4 connectivity to origins, despite accepting IPv6 connections from end users. Customers using CloudFront can configure their custom origins to use IPv4-only (default), IPv6-only, or dual-stack connectivity. When using dual-stack, CloudFront will automatically choose between IPv4 and IPv6 addresses to ensure even distribution of traffic towards origin over both.
Customers can configure IPv6 origins in all supported AWS Commercial Regions. Customers can configure IPv6-only or dual-stack origins with CloudFront, excluding Amazon S3 and VPC origins. To learn more IPv6 support with CloudFront, visit the CloudFront documentation.
Amazon CloudFront expands its IPv6 capabilities by introducing support for IPv6 connectivity to origin servers, allowing customers to implement end-to-end IPv6 content delivery for their web applications. Support for IPv6 origins enables customers to send IPv6 traffic all the way to their origins, allowing them to meet their architectural and regulatory requirements for IPv6 adoption. End-to-end IPv6 support improves network performance for end users connecting over IPv6 networks, and also removes concerns for IPv4 address exhaustion for origin infrastructure. Previously, CloudFront only supported IPv4 connectivity to origins, despite accepting IPv6 connections from end users. Customers using CloudFront can configure their custom origins to use IPv4-only (default), IPv6-only, or dual-stack connectivity. When using dual-stack, CloudFront will automatically choose between IPv4 and IPv6 addresses to ensure even distribution of traffic towards origin over both. Customers can configure IPv6 origins in all supported AWS Commercial Regions. Customers can configure IPv6-only or dual-stack origins with CloudFront, excluding Amazon S3 and VPC origins. To learn more IPv6 support with CloudFront, visit the CloudFront documentation.
Today, we are announcing a new capability that NetworkX now supports Neptune Analytics as a graph store. With this release, developers can continue to use familiar NetworkX APIs while automatically offloading graph algorithm workloads to Neptune’s scalable, high-performance analytics engine. This makes it simple to scale graph computations on demand without refactoring code, combining the ease of local development with the performance and elasticity of a fully managed AWS service.
Previously, when datasets grew beyond the limits of a local environment, users had to turn to third-party services—rebuilding their graph models to fit proprietary formats, exporting and importing data, and learning entirely new systems. With the new nx-neptune integration, developers only need an AWS account and credentials; the solution automatically handles graph data modeling, data movement (Zero-ETL), and infrastructure management. It provisions a Neptune Analytics instance, runs the requested algorithm, returns results directly to the user, and then tears down the infrastructure for a cost-effective, serverless-like experience—all without requiring the user to leave their familiar Python workflow.
NetworkX is a widely used open-source Python library for creating, analyzing, and visualizing complex graphs. It offers an extensive collection of graph algorithms and utilities, making it a popular choice among researchers, data scientists, and developers for prototyping and experimenting with graph-based applications. To learn more about the Neptune–NetworkX Integration, visit the documentation.
Today, we are announcing a new capability that NetworkX now supports Neptune Analytics as a graph store. With this release, developers can continue to use familiar NetworkX APIs while automatically offloading graph algorithm workloads to Neptune’s scalable, high-performance analytics engine. This makes it simple to scale graph computations on demand without refactoring code, combining the ease of local development with the performance and elasticity of a fully managed AWS service. Previously, when datasets grew beyond the limits of a local environment, users had to turn to third-party services—rebuilding their graph models to fit proprietary formats, exporting and importing data, and learning entirely new systems. With the new nx-neptune integration, developers only need an AWS account and credentials; the solution automatically handles graph data modeling, data movement (Zero-ETL), and infrastructure management. It provisions a Neptune Analytics instance, runs the requested algorithm, returns results directly to the user, and then tears down the infrastructure for a cost-effective, serverless-like experience—all without requiring the user to leave their familiar Python workflow. NetworkX is a widely used open-source Python library for creating, analyzing, and visualizing complex graphs. It offers an extensive collection of graph algorithms and utilities, making it a popular choice among researchers, data scientists, and developers for prototyping and experimenting with graph-based applications. To learn more about the Neptune–NetworkX Integration, visit the documentation.
