The Amazon SageMaker lakehouse architecture now automates optimization of Apache Iceberg tables stored in Amazon S3 with catalog-level configuration, reducing metadata overhead and improving query performance. Previously, optimizing Iceberg tables in AWS Glue Data Catalog required updating configurations for each table individually. Now, you can enable automatic optimization for new Iceberg tables with a one-time Data Catalog configuration. Once enabled, for any new table or updated table, Data Catalog continuously optimizes tables by compacting small files, removing snapshots, and unreferenced files that are no longer needed, resulting in controlled storage costs and faster queries.

You can get started by selecting the default catalog in the AWS Lake Formation console and enabling optimizations in the table optimizations configuration tab. You have the choice of additional granular control at the table configuration level, such as sort/z-order compaction strategy, thresholds for the number of small files to trigger compaction, intervals between consecutive snapshot expirations, and unreferenced data cleanup operations.

This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in 15 AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland, London, Frankfurt, Stockholm), Asia Pacific (Tokyo, Seoul, Mumbai, Singapore, Sydney, Jakarta), and South America (São Paulo). To learn more, read the blog, and visit the Data Catalog documentation.

​The Amazon SageMaker lakehouse architecture now automates optimization of Apache Iceberg tables stored in Amazon S3 with catalog-level configuration, reducing metadata overhead and improving query performance. Previously, optimizing Iceberg tables in AWS Glue Data Catalog required updating configurations for each table individually. Now, you can enable automatic optimization for new Iceberg tables with a one-time Data Catalog configuration. Once enabled, for any new table or updated table, Data Catalog continuously optimizes tables by compacting small files, removing snapshots, and unreferenced files that are no longer needed, resulting in controlled storage costs and faster queries.
You can get started by selecting the default catalog in the AWS Lake Formation console and enabling optimizations in the table optimizations configuration tab. You have the choice of additional granular control at the table configuration level, such as sort/z-order compaction strategy, thresholds for the number of small files to trigger compaction, intervals between consecutive snapshot expirations, and unreferenced data cleanup operations.
This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in 15 AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland, London, Frankfurt, Stockholm), Asia Pacific (Tokyo, Seoul, Mumbai, Singapore, Sydney, Jakarta), and South America (São Paulo). To learn more, read the blog, and visit the Data Catalog documentation.  

Amazon CloudWatch RUM, which enables customers to monitor their web applications by collecting client side performance and error data in real time, is additionally available in the following AWS Regions starting today: Asia Pacific (Thailand), and Mexico (Central).

CloudWatch RUM provides curated dashboards for web application performance experienced by real end users including anomalies in page load steps, core web vitals, and JavaScript and HTTP errors across different geolocations, browsers, and devices. Custom events and metrics sent to CloudWatch RUM can be easily configured to monitor specific parts of the application for real user interactions, troubleshoot issues, and get alerted for anomalies. CloudWatch RUM comes integrated with the application performance monitoring (APM) capability, CloudWatch Application Signals. As a result, client-side data from your application can easily be correlated with performance metrics such as errors, faults, and latency observed in your APIs (service operations) and dependencies to address the root cause.

To get started, see the RUM User Guide. Usage of CloudWatch RUM is charged on the number of collected RUM events, which refers to each data item collected by the RUM web client, as detailed here. 

​Amazon CloudWatch RUM, which enables customers to monitor their web applications by collecting client side performance and error data in real time, is additionally available in the following AWS Regions starting today: Asia Pacific (Thailand), and Mexico (Central). CloudWatch RUM provides curated dashboards for web application performance experienced by real end users including anomalies in page load steps, core web vitals, and JavaScript and HTTP errors across different geolocations, browsers, and devices. Custom events and metrics sent to CloudWatch RUM can be easily configured to monitor specific parts of the application for real user interactions, troubleshoot issues, and get alerted for anomalies. CloudWatch RUM comes integrated with the application performance monitoring (APM) capability, CloudWatch Application Signals. As a result, client-side data from your application can easily be correlated with performance metrics such as errors, faults, and latency observed in your APIs (service operations) and dependencies to address the root cause. To get started, see the RUM User Guide. Usage of CloudWatch RUM is charged on the number of collected RUM events, which refers to each data item collected by the RUM web client, as detailed here.   

Amazon SageMaker HyperPod now offers continuous provisioning, a new capability that enables greater flexibility and efficiency for enterprise customers running large-scale AI/ML workloads. AI/ML customers need to start training quickly, scale seamlessly, perform maintenance without disrupting operations, and have granular visibility into cluster operations. Customers also require the ability to efficiently manage dynamic inference workloads where capacity needs change frequently, making operational agility critical for successful AI initiatives.

With continuous provisioning, SageMaker HyperPod automatically provisions remaining capacity in the background while training jobs can begin immediately on available instances. HyperPod will retry in the background when it encounters node provisioning failures and ensure clusters reliably reach their desired scale without requiring any manual intervention. This helps customers reduce time-to-training and maximizes resource utilization across dynamic workloads. You can now perform concurrent operations such as scaling nodes independently, applying patches, or adjusting different instance groups simultaneously, thus increasing efficiency. The enhanced event-driven architecture provides comprehensive real-time visibility through the new Events APIs, offering complete operational history to enable faster troubleshooting and better decision-making. These capabilities enable customers to achieve improved operational agility, better resource utilization, and enhanced visibility into cluster operations, allowing AI/ML teams to focus on innovation rather than infrastructure management.

This feature is currently available for SageMaker HyperPod clusters using the EKS orchestrator. You can enable continuous provisioning by setting the NodeProvisioningMode parameter to «Continuous» when creating new HyperPod clusters using the CreateCluster API.

This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about continuous provisioning, see the Amazon SageMaker HyperPod User Guide.

​Amazon SageMaker HyperPod now offers continuous provisioning, a new capability that enables greater flexibility and efficiency for enterprise customers running large-scale AI/ML workloads. AI/ML customers need to start training quickly, scale seamlessly, perform maintenance without disrupting operations, and have granular visibility into cluster operations. Customers also require the ability to efficiently manage dynamic inference workloads where capacity needs change frequently, making operational agility critical for successful AI initiatives. With continuous provisioning, SageMaker HyperPod automatically provisions remaining capacity in the background while training jobs can begin immediately on available instances. HyperPod will retry in the background when it encounters node provisioning failures and ensure clusters reliably reach their desired scale without requiring any manual intervention. This helps customers reduce time-to-training and maximizes resource utilization across dynamic workloads. You can now perform concurrent operations such as scaling nodes independently, applying patches, or adjusting different instance groups simultaneously, thus increasing efficiency. The enhanced event-driven architecture provides comprehensive real-time visibility through the new Events APIs, offering complete operational history to enable faster troubleshooting and better decision-making. These capabilities enable customers to achieve improved operational agility, better resource utilization, and enhanced visibility into cluster operations, allowing AI/ML teams to focus on innovation rather than infrastructure management. This feature is currently available for SageMaker HyperPod clusters using the EKS orchestrator. You can enable continuous provisioning by setting the NodeProvisioningMode parameter to «Continuous» when creating new HyperPod clusters using the CreateCluster API. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about continuous provisioning, see the Amazon SageMaker HyperPod User Guide.  

Today, Amazon Elastic Kubernetes Service (Amazon EKS) expands support for Cilium as the Container Networking Interface (CNI) for Amazon EKS Hybrid Nodes. Cilium is a Cloud-Native Computing Foundation (CNCF) graduated project that provides core networking capabilities for Kubernetes workloads. Now, you can receive support from AWS for a broader set of Cilium features when using Cilium with Amazon EKS Hybrid Nodes including application ingress, in-cluster load balancing, Kubernetes network policies, and kube-proxy replacement mode.

Kubernetes clusters require a CNI for connectivity between pods running in the cluster, but most Kubernetes applications require additional components, such as ingress controllers and load balancers, to serve and secure network traffic with other external systems or users. These additional capabilities are integrated features of Cilium, built on Cilium’s eBPF-powered networking and security. Now, Amazon EKS Hybrid Nodes users can receive support from AWS for Cilium’s Ingress and Gateway features, Border Gateway Protocol (BGP) Control Plane, Load Balancer IP Address Management (LB IPAM), kube-proxy replacement, and Kubernetes network policies.

AWS supports the Amazon VPC CNI for Amazon EKS nodes in AWS Cloud, which is optimized for Amazon VPC networking with built-in features such as enhanced subnet discovery, Kubernetes network policies, and multiple network interfaces per pod. Cilium support for Amazon EKS Hybrid Nodes is available in all AWS Regions where Amazon EKS Hybrid Nodes is available. To learn more about Cilium support for Amazon EKS Hybrid Nodes, see Configure CNI for hybrid nodes in the Amazon EKS User Guide.

​Today, Amazon Elastic Kubernetes Service (Amazon EKS) expands support for Cilium as the Container Networking Interface (CNI) for Amazon EKS Hybrid Nodes. Cilium is a Cloud-Native Computing Foundation (CNCF) graduated project that provides core networking capabilities for Kubernetes workloads. Now, you can receive support from AWS for a broader set of Cilium features when using Cilium with Amazon EKS Hybrid Nodes including application ingress, in-cluster load balancing, Kubernetes network policies, and kube-proxy replacement mode. Kubernetes clusters require a CNI for connectivity between pods running in the cluster, but most Kubernetes applications require additional components, such as ingress controllers and load balancers, to serve and secure network traffic with other external systems or users. These additional capabilities are integrated features of Cilium, built on Cilium’s eBPF-powered networking and security. Now, Amazon EKS Hybrid Nodes users can receive support from AWS for Cilium’s Ingress and Gateway features, Border Gateway Protocol (BGP) Control Plane, Load Balancer IP Address Management (LB IPAM), kube-proxy replacement, and Kubernetes network policies. AWS supports the Amazon VPC CNI for Amazon EKS nodes in AWS Cloud, which is optimized for Amazon VPC networking with built-in features such as enhanced subnet discovery, Kubernetes network policies, and multiple network interfaces per pod. Cilium support for Amazon EKS Hybrid Nodes is available in all AWS Regions where Amazon EKS Hybrid Nodes is available. To learn more about Cilium support for Amazon EKS Hybrid Nodes, see Configure CNI for hybrid nodes in the Amazon EKS User Guide.  

The Amazon ECS console now natively integrates with Amazon CloudWatch Logs Live Tail, enabling real-time log streaming directly within the ECS console.

ECS customers often need to analyze logs in real time to troubleshoot application issues, investigate deployment failures, and monitor container health. Previously, customers had to navigate away from the ECS console to the CloudWatch console to access real-time log streams, creating workflow interruptions. With this integration, ECS customers can now monitor and troubleshoot containerized applications in real-time without switching between AWS consoles.

To start tailing logs in the ECS console, navigate to the logs tab on any ECS service or task details page. Simply click the “Open CloudWatch Logs Live Tail” button, and click “Start”. The Live Tail panel remains visible as you navigate the console, enabling log monitoring while checking other operational metrics.

Live Tail in the ECS console is now available in all AWS commercial regions. To learn more, visit the ECS developer guide.

​The Amazon ECS console now natively integrates with Amazon CloudWatch Logs Live Tail, enabling real-time log streaming directly within the ECS console. ECS customers often need to analyze logs in real time to troubleshoot application issues, investigate deployment failures, and monitor container health. Previously, customers had to navigate away from the ECS console to the CloudWatch console to access real-time log streams, creating workflow interruptions. With this integration, ECS customers can now monitor and troubleshoot containerized applications in real-time without switching between AWS consoles. To start tailing logs in the ECS console, navigate to the logs tab on any ECS service or task details page. Simply click the “Open CloudWatch Logs Live Tail” button, and click “Start”. The Live Tail panel remains visible as you navigate the console, enabling log monitoring while checking other operational metrics. Live Tail in the ECS console is now available in all AWS commercial regions. To learn more, visit the ECS developer guide.  

• Categoría: Noticias de Microsoft

agosto 8, 2025

Comprender lo que la IA significa para los consumidores

Por: Nicci Trovinger, gerente general, Windows.

Cuando hablamos de nuevos dispositivos y experiencias impulsados por IA, la atención a menudo se centra en el ritmo del progreso tecnológico. Pero con la misma rapidez, la forma en que las personas usan estas herramientas, y cómo se sienten al respecto, también está en evolución.

Para comprender mejor ese sentimiento cambiante, encargamos una nueva investigación de IA del consumidor que profundiza en las prioridades y percepciones de las personas. Más allá de los datos de uso, examinamos las corrientes emocionales subyacentes: qué entusiasma a las personas sobre la IA, qué les hace reflexionar y cómo esas actitudes cambian a través de las generaciones.

Lo que surgió es una visión más texturizada del comportamiento del consumidor. En este informe, encontrarán información que agrega una mayor dimensión a cómo se ven las soluciones de IA significativas en la actualidad.

El informe completo está disponible para descargar aquí. 

Del agotamiento al avance: los estadounidenses usan la IA para avanzar

Este informe de IA del consumidor examina la evolución de las actitudes hacia la IA. Presenta los hallazgos de una investigación realizada por una firma de investigación independiente, Edelman Data & Intelligence, entre 1,000 consumidores en los Estados Unidos mayores de 13 años entre el 14 de marzo de 2025 y el 25 de marzo de 2025. A medida que las herramientas de IA y los comportamientos humanos continúan con sus cambios, el informe ofrece una lente respaldada por la investigación para líderes empresariales, organizaciones y personas curiosas que buscan comprender qué ha cambiado y por qué.

¿Puede la IA ayudar a una generación sobrecargada a eliminar el ruido?

En estos días, tenemos muchas cosas en mente.

Vivimos en una era en la que la información nunca ha estado tan disponible. Historias enteras de sociedades, cuerpos de erudición e incluso los detalles de nuestras propias relaciones se pueden obtener con una sola búsqueda. Pero en lugar de ayudarnos a salir adelante, a menudo solo agrega más ruido. La autoridad tradicional se ha fracturado y dondequiera que miremos, nuevas voces y plataformas compiten por nuestra atención. De hecho, 7 de cada 10 consumidores admiten que se sienten abrumados por la cantidad de información disponible al tomar una decisión.

Por lo tanto, no sorprende que hayamos comenzado a cuestionar no solo nuestras elecciones, sino también cómo las hacemos. Aquí es donde la IA ofrece un nuevo camino a seguir: nuestra investigación encuentra que contrarresta la fatiga de decisión al aligerar la carga mental de sopesar las propias opciones. Después de usar IA al tomar una decisión, el 84% de las personas informan haber experimentado emociones positivas.

Presentamos a la Generación IA

A la cabeza está la Generación IA, nacida entre 1995 y 2012. Criados con herramientas digitales cada vez más intuitivas, han aprendido a adoptar las tecnologías emergentes como un sistema de apoyo en lugar de solo un atajo, desde PC y dispositivos móviles hasta Internet y ahora IA. Esta generación tiene un 16% más de probabilidades de usar herramientas de IA que las mayores, y cuando lo hacen, encuentran más que respuestas. Desbloquean una mayor sensación de alivio y confianza, un resultado del que los usuarios de todas las edades pueden aprender.

La IA interrumpe el pensamiento excesivo, antes de que comience la espiral

El momento principal de la IA llega en un momento crítico para la salud mental de esta generación.

La generación de IA lleva una carga compuesta por el peso ambiental de las presiones sociales cotidianas, la incertidumbre económica persistente, el aislamiento digital y la larga cola de una pandemia global. El setenta y dos por ciento de las personas de 18 a 34 años califican la salud mental como un factor estresante significativo, el más alto entre todas las cohortes de edad.  

Con estimaciones que sugieren que la persona promedio puede enfrentar miles de opciones cada día, esta carga mental es implacable. Es el tipo de peso que convierte la indecisión en inacción, lo que lleva a las personas a abandonar opciones que alguna vez se sintieron importantes.  

Incluso una vez que por fin podemos decidirnos, rara vez se siente como un cierre. El sesenta y ocho por ciento de la Generación AI se describiría a sí mismo como un «pensador excesivo», alguien que pasa mucho tiempo preocupándose por sus decisiones, incluso después de tomarlas. El posible alivio se ve empañado por la duda, una sensación persistente de que tal vez nos perdimos algo mejor, más inteligente o más optimizado.

Pero los datos muestran que la IA ofrece a los pensadores excesivos un resultado diferente. En todos los grupos de edad, los encuestados tenían más del doble de probabilidades de sentirse aliviados (30%) o confiados (30%) en comparación con ansiosos (14%) o frustrados (14%) después de usar IA generativa para tomar una decisión personal.

Este aumento de confianza se aplica a una variedad de escenarios identificables. Muchos encuentran apoyo para las cosas que les apasionan e involucran a la IA en decisiones sobre entretenimiento (34%) o viajes (25%). Para otros, la IA resulta útil para moverse por territorios más cargados a nivel emocional, como decisiones monetarias (35%), salud y bienestar (35%) y consideraciones profesionales o laborales (34%).

En lugar de detenerse en estas decisiones de manera interminable, cada prompt se convierte en una práctica silenciosa para convertir la incertidumbre en acción.

Crear un espacio seguro para respuestas más profundas y útiles

Ahora vislumbramos un futuro impulsado por la tecnología que es más intuitivo, personal y libre de juicios. La IA refleja la curiosidad de los consumidores de una manera que pocas herramientas lo han hecho antes. Cuando necesitan ayuda para tomar una decisión, un tercio de los encuestados (33%) dice que aprecia que la IA les brinde una respuesta clara y personalizada.

Obtener el asesoramiento adecuado siempre ha dependido de los guardianes del momento. En el pasado, la información estaba limitada por los expertos o instituciones a los que se tenía acceso. Incluso Internet, una vez visto como el gran ecualizador, tiene sus límites. Los motores de búsqueda que la Generación IA uso mientras crecía, pueden haber puesto páginas y páginas de resultados web al alcance de su mano, pero no lograron convertir esos datos en algo en verdad procesable. Esto ha dejado al 67% de este grupo de edad con la sensación de que todavía es «difícil encontrar orientación o sugerencias que se ajusten a mi situación exacta» al recopilar información para responder una pregunta o tomar una decisión.

Ahora, tienen otro lugar al que acudir; un asesor conversacional que pueda combinar su sed de conocimiento con especificidad, flexibilidad y paciencia. Cuando se les preguntó sobre el uso de la IA generativa para obtener consejos, todos los encuestados citaron una sensación de delicadeza emocional, y la señalan cómo «puedo hacer tantas preguntas de seguimiento como quiera sin sentirme mal» (81%) y «la IA no me juzga como lo haría una persona» (78%).

Este cambio en nuestra relación con la información también cambia la forma en que aprendemos. Una investigación reciente sobre el uso de la IA encontró que los estudiantes de 18 años o más la usaban más que cualquier otro grupo de empleo, y el 85% informó su uso. Los estudiantes de la Generación AI ahora tienen más probabilidades de clasificar la IA como una ayuda útil para el estudio (45%) que los libros (36%) o un tutor individual (27%). 

La forma en que los usuarios de IA se describen a sí mismos nos dice más sobre su mentalidad. Aquellos que usan IA para tomar decisiones son más propensos a decir que son «ambiciosos» (+20ppts), «decisivos» (+15ppts) y «solucionadores de problemas» (+10 ppts) en comparación con aquellos que no la usan. Estas etiquetas señalan cómo la IA podría cruzarse con el sentido de identidad de una generación.

Si bien cada interacción individual puede parecer pequeña, estos micro momentos de apoyo pueden fomentar la confianza tanto en la tecnología en sí como en la propia capacidad de elección del usuario.

Esperanzadora pero no ingenua, la Generación IA aporta discernimiento a las preguntas de la IA

Esta no es la primera vez que la Generación AI ha vivido un cambio tecnológico importante, y no será la última. Como verdaderos nativos digitales, abordan cualquier nueva herramienta con matices y sopesan de manera cuidadosa los beneficios prometidos frente a las posibles compensaciones.

Cuando se trata de IA, el 66% de esta generación es optimista de que mejorará nuestras vidas y el mundo en el que vivimos. Si bien solo el 15% de todos los consumidores dicen que confían a plenitud en la IA al tomar decisiones importantes, el 95% todavía ha utilizado una herramienta de IA generativa en el último mes, lo que sugiere que las personas han encontrado formas significativas y apropiadas de interactuar con estas herramientas. En lugar de confianza ciega, esta es una adopción reflexiva: los usuarios integran la IA en su proceso de toma de decisiones más amplio de manera que se sientan de apoyo y seguridad.

¿También en la mezcla? Amigos, familiares, expertos y profesionales. Pero, sobre todo, su propio juicio: el 59% de los consumidores confía en su instinto a la hora de tomar una decisión.

Llámenlo curiosidad, precaución o una mezcla equilibrada de manera cuidadosa de ambos. Si bien el 59% de todos los encuestados utilizaron IA generativa para fines laborales y comerciales en el último año, aún más han explorado cómo podría encajar en sus vidas personales. El sesenta y cuatro por ciento informa que usa IA para pasatiempos e intereses personales, como arte, música o proyectos de bricolaje.

La IA puede ayudar a clasificar la sobrecarga de información actual hasta que los instintos de uno se hagan cargo. Resume la información para que sea más fácil de entender (34% de los casos de uso), muestra diferentes opciones en las que los usuarios no habían pensado (31%) y compara opciones mostrando pros y contras (30%).

Recurrir a la IA en estos momentos cotidianos genera un ritmo de confianza, medido, útil y, a menudo, acompañado de una sensación de alivio. Con la estructura suficiente para ayudar a las personas a dar sentido a las consideraciones urgentes, estas herramientas hacen posible una toma de decisiones segura.

En un mundo que a menudo parece demasiado, la IA ofrece algo raro: alivio

Nuestra investigación muestra que los consumidores estadounidenses alivian de manera emocional la toma de decisiones al reforzar su propio juicio con herramientas impulsadas por IA que ofrecen claridad, curiosidad y calma.

La IA remodela lo que se siente al elegir. El «antes», esa fase de recopilación de datos, es más corta, más ágil. La información se entrega de manera clara, sin sobrecarga ni juicio. El «después» también se siente diferente, marcado por la tranquilidad en lugar del arrepentimiento. En lugar de tomar la decisión correcta, las personas experimentan una fuerte sensación de confianza.

La prueba está en la práctica: usar estas herramientas como lo hace la Generación IA, para decisiones cotidianas tanto grandes como pequeñas, cambia lo que es posible. Con el tiempo, genera el tipo de impulso que mueve a las personas a través de la incertidumbre, no solo a su alrededor. Y cuando se enfrentan al ritmo diario de las decisiones, les ayuda a confiar en sí mismos lo suficiente como para seguir adelante.

Etiquetas:

• IA
• Vida y Trabajo

The post Comprender lo que la IA significa para los consumidores appeared first on Source LATAM.

​The post Comprender lo que la IA significa para los consumidores appeared first on Source LATAM.  

Today, AWS announces the general availability of static networking configuration support for the service link and DNS IP addresses of AWS Outposts servers. This new feature enables customers to configure the service link interface and DNS IP addresses of their Outposts servers with static IP addresses during installation, eliminating the requirement for Dynamic Host Configuration Protocol (DHCP) servers in their data centers.

This enhancement is valuable for customers with stringent networking security requirements who cannot use DHCP servers in their data centers. These customers can now configure IP addresses manually for the service link connection while maintaining their security standards.

This feature is available in all AWS Regions where Outposts servers are supported.

Customers need to configure this feature during the Outposts servers installation. To learn more, visit the Outposts server installation guide.

​Today, AWS announces the general availability of static networking configuration support for the service link and DNS IP addresses of AWS Outposts servers. This new feature enables customers to configure the service link interface and DNS IP addresses of their Outposts servers with static IP addresses during installation, eliminating the requirement for Dynamic Host Configuration Protocol (DHCP) servers in their data centers.
This enhancement is valuable for customers with stringent networking security requirements who cannot use DHCP servers in their data centers. These customers can now configure IP addresses manually for the service link connection while maintaining their security standards.
This feature is available in all AWS Regions where Outposts servers are supported.
Customers need to configure this feature during the Outposts servers installation. To learn more, visit the Outposts server installation guide.  

Beginning today, customers can use Amazon Bedrock in the Asia Pacific (Melbourne) region to easily build and scale generative AI applications using a variety of foundation models (FMs) as well as powerful tools to build generative AI applications.

Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models and other FMs from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities, such as Guardrails and Model customization, that customers need to build generative AI applications with security, privacy, and responsible AI built into Amazon Bedrock. These capabilities help customers build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance.

To get started, visit the Amazon Bedrock page and see the Amazon Bedrock documentation for more details.

​Beginning today, customers can use Amazon Bedrock in the Asia Pacific (Melbourne) region to easily build and scale generative AI applications using a variety of foundation models (FMs) as well as powerful tools to build generative AI applications. Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models and other FMs from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities, such as Guardrails and Model customization, that customers need to build generative AI applications with security, privacy, and responsible AI built into Amazon Bedrock. These capabilities help customers build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance. To get started, visit the Amazon Bedrock page and see the Amazon Bedrock documentation for more details.  

Amazon OpenSearch Service now supports Fine Grained Access Control (FGAC) for OpenSearch UI when accessed through SAML via IAM federated. OpenSearch UI is the unified interface for observability and security analytics on Amazon OpenSearch Service. SAML via IAM federated is a popular choice to enable Identity Provider (IdP) initiated Single Sign-On experience for accessing OpenSearch UI. FGAC enables you to define precise data access control based on user attributes provided from your IdP during SAML authentication and authorization. This level of dynamic and granular access control is crucial for multi-tenant deployments and meeting data governance requirement in regulated industries. 

With FGAC support, you can now configure attribute mappings from IdP user roles and attributes to OpenSearch backend roles. These roles can be scoped to specific OpenSearch domains and serverless collections, allowing you to define index-level permissions and document-level security for more granular data access controls. You can easily manage users and groups within your existing IdP, and OpenSearch data source permissions are automatically applied based on the user’s SAML assertion, reducing administrative friction. Furthermore, audit trails become clearer as user actions are tied not just to IAM roles but to SAML attributes, simplifying data governance. 

FGAC is an optional feature for SAML via IAM federated. It is available in all regions that OpenSearch UI is available. Learn more at: OpenSearch UI dev guide.

​Amazon OpenSearch Service now supports Fine Grained Access Control (FGAC) for OpenSearch UI when accessed through SAML via IAM federated. OpenSearch UI is the unified interface for observability and security analytics on Amazon OpenSearch Service. SAML via IAM federated is a popular choice to enable Identity Provider (IdP) initiated Single Sign-On experience for accessing OpenSearch UI. FGAC enables you to define precise data access control based on user attributes provided from your IdP during SAML authentication and authorization. This level of dynamic and granular access control is crucial for multi-tenant deployments and meeting data governance requirement in regulated industries.  With FGAC support, you can now configure attribute mappings from IdP user roles and attributes to OpenSearch backend roles. These roles can be scoped to specific OpenSearch domains and serverless collections, allowing you to define index-level permissions and document-level security for more granular data access controls. You can easily manage users and groups within your existing IdP, and OpenSearch data source permissions are automatically applied based on the user’s SAML assertion, reducing administrative friction. Furthermore, audit trails become clearer as user actions are tied not just to IAM roles but to SAML attributes, simplifying data governance. 
FGAC is an optional feature for SAML via IAM federated. It is available in all regions that OpenSearch UI is available. Learn more at: OpenSearch UI dev guide.
   

Amazon Elastic Kubernetes Service (EKS) now supports deletion protection, helping you prevent accidental termination of your EKS clusters. When enabled, deletion protection requires explicit disablement before a cluster can be deleted, providing an additional safety control for critical environments.

Deletion protection is turned off by default for all new and existing clusters. You can enable deletion protection during cluster creation or any time after. To delete a protected cluster, you must first disable deletion protection for the cluster and then proceed with the cluster deletion. This two-step verification process helps prevent unintended deletions that could result from automation errors or accidental commands, especially in environments where multiple users share cluster management responsibilities.

Once enabled, any attempt to delete the cluster through the AWS Management Console, EKS APIs, AWS Command Line Interface (CLI), eksctl, or infrastructure as code tools like AWS CloudFormation will be blocked until deletion protection is disabled. This feature is available in all commercial AWS Regions and the AWS GovCloud (US) Regions. To learn more, visit the Amazon EKS documentation.

​Amazon Elastic Kubernetes Service (EKS) now supports deletion protection, helping you prevent accidental termination of your EKS clusters. When enabled, deletion protection requires explicit disablement before a cluster can be deleted, providing an additional safety control for critical environments. Deletion protection is turned off by default for all new and existing clusters. You can enable deletion protection during cluster creation or any time after. To delete a protected cluster, you must first disable deletion protection for the cluster and then proceed with the cluster deletion. This two-step verification process helps prevent unintended deletions that could result from automation errors or accidental commands, especially in environments where multiple users share cluster management responsibilities. Once enabled, any attempt to delete the cluster through the AWS Management Console, EKS APIs, AWS Command Line Interface (CLI), eksctl, or infrastructure as code tools like AWS CloudFormation will be blocked until deletion protection is disabled. This feature is available in all commercial AWS Regions and the AWS GovCloud (US) Regions. To learn more, visit the Amazon EKS documentation.