• Categoría: Noticias de Microsoft

agosto 28, 2025

Informe sobre la IA en la educación: Perspectivas para apoyar la enseñanza y el aprendizaje

Por: Equipo de Microsoft Educación.

Desde pequeños cambios en el aula hasta estrategias más grandes para todo el sistema, la IA se ha comenzado a convertir en parte del futuro de la educación. La conversación ya no es «si» sino «cómo», ya que las instituciones consideran lo que eso significa para los estudiantes, educadores e instituciones. Para comprender mejor este cambio, realizamos numerosos estudios y encuestas y colaboramos con instituciones y organizaciones académicas. El Informe sobre la IA en la Educación 2025 explora el uso actual de la IA, las oportunidades emergentes y los principales ejemplos, y lo que sigue para su papel en la educación.

Aquí hay cuatro conclusiones clave del Informe de IA en la Educación 2025:

1. La adopción de la IA se acelera en la educación, pero la capacitación no ha seguido el ritmo.
2. La IA puede ser un socio creativo y colaborativo, al complementar, no reemplazar, los métodos de aprendizaje tradicionales.
3. La fluidez de la IA es un imperativo de la fuerza laboral, con una presión creciente sobre las instituciones para preparar a los estudiantes en consecuencia.
4. La IA ayuda a reimaginar las experiencias de aprendizaje, pero los desafíos en torno al uso responsable y la preparación deben abordarse de frente.

También hemos encuestado a líderes académicos y de TI, educadores y estudiantes de todo el mundo: exploren los datos detallados de la encuesta del Informe de IA en la educación para obtener más información.

1. La adopción de la IA se acelera en la educación

Los hallazgos del informe muestran que el uso de la IA en la educación ha aumentado, con el 86% de las organizaciones educativas que ahora utilizan IA generativa, la tasa más alta de cualquier industria.¹ En los Estados Unidos:

• El uso de IA por parte de los estudiantes para la escuela aumentó 26 puntos porcentuales con respecto al año pasado.
• El uso por parte de educadores aumentó 21 puntos porcentuales.

Desde el aprendizaje personalizado en las aulas K-12 hasta las herramientas administrativas impulsadas por IA en las universidades, las instituciones han comenzado a integrar con rapidez la IA para mejorar la eficiencia, el compromiso y los resultados.

Pero parece que el entrenamiento de IA no ha seguido el ritmo:

• Menos de la mitad de los estudiantes estadounidenses y los educadores globales dicen que saben mucho sobre IA.
• A nivel internacional, el 76% de los líderes dicen que la mitad o más de los usuarios de IA en su institución han recibido capacitación en IA.
• Sin embargo, el 45% de los educadores a nivel mundial y el 52% de los estudiantes estadounidenses dicen que no han recibido ninguna capacitación.

Este desajuste señala una brecha de percepción entre lo que los líderes creen que han entregado y lo que los estudiantes y educadores sienten que han recibido.

Recomendaciones

• Compartan la ruta de aprendizaje de IA para educadores para ayudar a los educadores a desarrollar confianza y habilidades prácticas para usar la IA en su práctica diaria.
• Ofrezcan un aprendizaje profesional continuo y diferenciado por medio de AI Skills Navigator.
• Usen el kit de herramientas de IA de Microsoft Education para guiar la planeación estratégica en toda la institución.

Descargar el kit de herramientas de IA de Microsoft Education

2. La IA es un socio creativo y colaborativo

La IA ayuda a los estudiantes, educadores y líderes educativos a pensar de manera más creativa y trabajar de manera más colaborativa. El Informe sobre la IA en la educación de 2025 muestra que:

• Los estudiantes utilizan la IA para hacer una lluvia de ideas sobre las tareas (37%), resumir información (33%) y recibir comentarios (32%).
• Los educadores lo utilizan para intercambiar ideas, crear y actualizar lecciones (31%), simplificar temas complejos (24%) y diferenciar la instrucción (23%), lo que libera tiempo para concentrarse en la participación de los estudiantes.
• Los líderes lo utilizan para optimizar las operaciones (35%), proporcionar herramientas de accesibilidad (33%) e identificar oportunidades para el crecimiento de los estudiantes (33%).

Estos usos ayudan a los líderes y educadores a liberar tiempo para la participación, capacitar a los estudiantes para que aprendan de la manera que mejor les funcione y fomentar la exploración creativa para todos.

Si bien el uso de la IA está en crecimiento, los hallazgos del informe muestran que es más efectiva cuando se usa para complementar, no reemplazar, los métodos de aprendizaje tradicionales. Un estudio de Microsoft Research y Cambridge University Press & Assessment encontró que los estudiantes que combinaron herramientas de IA con toma de notas y otros métodos aprendieron más que aquellos que confiaron solo en la IA.

Recomendaciones

• Usen Microsoft Learning Accelerators con tecnología de IA  para complementar la instrucción, para brindar a los estudiantes comentarios en tiempo real y ayudándolos a guiar su aprendizaje.
• Fomenten la experimentación con Microsoft 365 Copilot y Copilot Chat para explorar nuevas formas de mejorar, no reemplazar, los métodos de aprendizaje tradicionales.

Comiencen con Copilot en la educación

Usamos Copilot Chat como un socio de lluvia de ideas para idear, pero no para hacer nuestro trabajo por nosotros. Nos ayuda a colaborar y expandir nuestra creatividad para pensar en ideas más ambiciosas.

– Pragya Modgil, estudiante, Escuela Secundaria Johns Creek, Escuelas del Condado de Fulton

3. La fluidez de la IA es un imperativo de la fuerza laboral

La fluidez de la IA se ha comenzado a convertir en una prioridad para los nuevos trabajadores, junto con habilidades en esencia humanas, como la mitigación de conflictos y la adaptabilidad. Juntas, estas habilidades serán esenciales cuando los estudiantes ingresen al nuevo mundo laboral. El informe señala que:

• Más del 47% de los líderes consideran que mejorar las habilidades de los empleados en IA es la principal estrategia de fuerza laboral para los próximos 12 a 18 meses.
• El 66% de los líderes dicen que no contratarían a alguien sin habilidades de alfabetización en IA.

Los educadores y líderes educativos reconocen la necesidad de una mayor capacitación en habilidades de IA, con el 54% de los educadores globales y el 76% de los líderes globales que ven la alfabetización en IA como un componente esencial de la educación básica para todos los estudiantes. Los estudiantes de hoy deberán poder:

• Sepan cómo usar la IA como un asistente, no como una herramienta.
• Aprendan cómo y cuándo delegar a la IA y cuándo involucrar a las personas.
• Piensen como gerentes, ya que es probable que administren la IA.

Recomendaciones

• Exploren enfoques institucionales como el curso «Enseñanza con IA» en la Universidad de Auburn o el Programa Waterloo Experience Accelerate en la Universidad de Waterloo.
• Utilicen las actividades inmersivas de Minecraft Education AI Foundations para enseñar a los estudiantes habilidades de IA técnicas y centradas en el ser humano.
• Transformen la enseñanza y apoyen la preparación para el trabajo con Copilot Chat, disponible para estudiantes mayores de 13 años.

Prueben Copilot Chat

4. La IA ayuda a reimaginar las experiencias de aprendizaje

Ya sea a través de apoyar a los estudiantes neurodivergentes, facilitar la comunicación multilingüe o actuar como un socio de lluvia de ideas, la IA capacita a los estudiantes y educadores para explorar nuevas formas de pensar y aprender juntos. En un estudio realizado en el Reino Unido, estudiantes universitarios y educadores dijeron que la IA les ayudó a superar bloqueos creativos, explorar nuevos temas y aclarar contenido complejo, describiéndolo como un socio colaborativo disponible durante todo el día.

Sin embargo, si bien la IA amplía nuevas posibilidades en la educación, realizar todo su potencial requiere abordar las preocupaciones que plantea. El Informe sobre la IA en la Educación 2025 arroja luz sobre el panorama en evolución:

• Los estudiantes están más preocupados por ser acusados de plagio o trampa (33%) y volverse demasiado dependientes de la IA (30%).
• La principal preocupación de los educadores es el plagio (31%), seguido de la dependencia excesiva (21%), la desinformación (20%), la seguridad (20%) y la capacitación insuficiente (20%).
• Los líderes están más preocupados por las preocupaciones éticas (21%), la falta de preparación para TI (20%) y el acceso equitativo (18%).

Las recomendaciones del informe para abordar estas preocupaciones incluyen fomentar la comunicación abierta, apoyarse en oportunidades de capacitación siempre activas y crear un espacio para que su comunidad comparta y reflexione. Juntas, estas acciones pueden ayudar a construir una cultura más informada, inclusiva y segura.

Recomendaciones

• Lean el informe técnico de IDC, A Blueprint for AI-Ready Campuses, patrocinado por Microsoft, para obtener recomendaciones organizativas, de procesos y tecnológicas para dar forma y avanzar en la estrategia de IA.²
• Exploren historias del mundo real de IA en la educación de World Traveler School, Brisbane Catholic Education y Kelley School of Business en la Universidad de Indiana.
• Comiencen con las soluciones de inteligencia artificial de Microsoft Education para ampliar las posibilidades en su institución.

Descubran el potencial de la IA en la educación

Crear oportunidades a través de la IA en la educación

Si bien la familiaridad y el uso de la IA son altos en todos los grupos, persisten brechas. Las ideas de este informe apuntan a cuatro desafíos clave que enfrenta la IA en la educación:

• Adopción sin alineación: el uso generalizado de la IA ha comenzado a superar la capacitación y el entendimiento compartido entre educadores y estudiantes.
• Potencial creativo, optimismo cauteloso: las posibilidades con IA son inspiradoras, pero deben basarse en estrategias de enseñanza y aprendizaje comprobadas para ser efectivas.
• Necesidades de preparación de la fuerza laboral: las instituciones reconocen la importancia de la alfabetización en IA, pero necesitan apoyo práctico para integrarla de manera significativa en el plan de estudios y la instrucción.
• Reimaginar el aprendizaje de manera responsable: la IA ofrece un potencial emocionante, y darse cuenta de ese potencial requerirá involucrar a estudiantes y educadores para construir soluciones juntos a través de una comunicación abierta.

Para avanzar, los educadores, líderes y estudiantes deben trabajar juntos, adaptarse en tiempo real y comprometerse con el uso responsable de la IA. Los educadores y líderes no piden prohibiciones, piden un aprendizaje profesional de alta calidad e integrado en el trabajo.

Los maestros dicen: ‘Necesito capacitación, debe ser de alta calidad, relevante e integrada en el trabajo…’ En realidad, las personas requieren orientación y eso significa que los maestros y administradores pasen por el desarrollo profesional.

– Pat Yongpradit, director académico de Code.org y líder de TeachAI

La IA puede ser un poderoso compañero de pensamiento y multiplicador de fuerzas, que amplifica ideas, agiliza tareas y desbloquea nuevas posibilidades para la enseñanza y el aprendizaje. A medida que navegan por las oportunidades y complejidades de la IA, Microsoft Education está aquí para ayudarlos con herramientas, entrenamiento e información. Exploren el Informe completo de IA en la educación de 2025 para profundizar en los datos y utilizar los recursos de este blog para respaldar su propio viaje de IA.

Descargar el informe sobre la IA en la educación

¹ IDC InfoBrief: patrocinado por Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, noviembre de 2024. Estudio de oportunidades de IA 2024 de IDC: las cinco principales tendencias de IA a tener en cuenta – Blog Oficial de Microsoft

² Informe técnico de IDC, patrocinado por Microsoft, A Blueprint for AI-Ready Campuses: Strategies from the Frontlines of Higher Education, IDC# US53344625, mayo de 2025. Campus preparados para la IA: Estrategias de la primera línea de la educación superior | Blog de Microsoft Education

Etiquetas:

• Educación
• IA
• Tecnología

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Amazon OpenSearch Serverless has added support for attribute-based authorization (ABAC) for Data Plane APIs, making it easier to manage access control for data read and write operations. This feature is part of an AWS campaign to drive consistent adoption of AWS Identity and Access Management (IAM) features across all AWS services. Customers can use identity policies in IAM to define permissions and control who has access to the data on Amazon OpenSearch Serverless collections.

Amazon OpenSearch Serverless now also supports resource control policy (RCP). RCP is a new type of authorization policy managed in AWS Organizations that will allow OpenSearch Serverless customers to enforce organization-wide preventative controls across resources in their organization centrally, without the need to update individual resource-based policies. You can refer to documentation for examples.

Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about OpenSearch Serverless, see the documentation. 

​Amazon OpenSearch Serverless has added support for attribute-based authorization (ABAC) for Data Plane APIs, making it easier to manage access control for data read and write operations. This feature is part of an AWS campaign to drive consistent adoption of AWS Identity and Access Management (IAM) features across all AWS services. Customers can use identity policies in IAM to define permissions and control who has access to the data on Amazon OpenSearch Serverless collections.
Amazon OpenSearch Serverless now also supports resource control policy (RCP). RCP is a new type of authorization policy managed in AWS Organizations that will allow OpenSearch Serverless customers to enforce organization-wide preventative controls across resources in their organization centrally, without the need to update individual resource-based policies. You can refer to documentation for examples. Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about OpenSearch Serverless, see the documentation.   

Today, Amazon Elastic Kubernetes Service (EKS) introduced support for on-demand refresh of cluster insights, enabling customers to more efficiently test and validate if applied recommendations have successfully taken effect.

Every Amazon EKS cluster undergoes automatic, periodic checks against a curated list of insights, which provide detection of issues, such as warnings about changes required before Kubernetes version upgrades, as well as recommendations for how to address each insight. With on-demand cluster insights refresh functionality, customers can fetch the latest insights immediately after making changes, accelerating the testing and verification process when performing upgrades or making configuration changes to your cluster.

EKS upgrade insights and the new refresh capability is available in all commercial AWS Regions. To learn more visit the EKS documentation.

​Today, Amazon Elastic Kubernetes Service (EKS) introduced support for on-demand refresh of cluster insights, enabling customers to more efficiently test and validate if applied recommendations have successfully taken effect. Every Amazon EKS cluster undergoes automatic, periodic checks against a curated list of insights, which provide detection of issues, such as warnings about changes required before Kubernetes version upgrades, as well as recommendations for how to address each insight. With on-demand cluster insights refresh functionality, customers can fetch the latest insights immediately after making changes, accelerating the testing and verification process when performing upgrades or making configuration changes to your cluster. EKS upgrade insights and the new refresh capability is available in all commercial AWS Regions. To learn more visit the EKS documentation.  

Today, AWS announces the general availability of account color settings in AWS Management Console across all Public Regions. AWS customers now have an easy way to identify their accounts at a glance. Using the account color setting, account admins can assign a color to their AWS account (such as red for production accounts or yellow for testing accounts) that appears in the Console’s navigation bar for all authorized users in that account, enabling quick visual identification of different accounts.

AWS customers manage multiple accounts to separate their workloads, such as maintaining distinct accounts for development and production environments or for different business units. Previously, users had to rely on account numbers to identify accounts. With this new feature, users with admin privileges can assign colors to AWS accounts, enabling all authorized users to quickly identify the account they want to operate on through the colored navigation bar.

When signing in to the AWS Console, users see a default grey color in the navigation bar. Users with admin privileges can change this color through the ‘Account’ option in the navigation bar’s account menu, on the top right of the page. Once the color is set, all users with proper permissions can see it. To view the account color, users need to be assigned permissions using the AWS managed policy AWSManagementConsoleBasicUserAccess or the custom permission uxc:getaccountcolor.

To learn more about account colors, click here. To get started with setting account color, click here.

​Today, AWS announces the general availability of account color settings in AWS Management Console across all Public Regions. AWS customers now have an easy way to identify their accounts at a glance. Using the account color setting, account admins can assign a color to their AWS account (such as red for production accounts or yellow for testing accounts) that appears in the Console’s navigation bar for all authorized users in that account, enabling quick visual identification of different accounts. AWS customers manage multiple accounts to separate their workloads, such as maintaining distinct accounts for development and production environments or for different business units. Previously, users had to rely on account numbers to identify accounts. With this new feature, users with admin privileges can assign colors to AWS accounts, enabling all authorized users to quickly identify the account they want to operate on through the colored navigation bar. When signing in to the AWS Console, users see a default grey color in the navigation bar. Users with admin privileges can change this color through the ‘Account’ option in the navigation bar’s account menu, on the top right of the page. Once the color is set, all users with proper permissions can see it. To view the account color, users need to be assigned permissions using the AWS managed policy AWSManagementConsoleBasicUserAccess or the custom permission uxc:getaccountcolor.
To learn more about account colors, click here. To get started with setting account color, click here.  

AWS Client VPN now supports Windows Arm64 client with version 5.3.0. You can now run the AWS supplied VPN client on the latest Windows Arm64 OS versions. AWS Client VPN desktop clients are available free of charge, and can be downloaded here.

AWS Client VPN is a managed service that securely connects your remote workforce to AWS or on-premises networks. It supports desktop clients for MacOS, Windows x64, Windows Arm64 and Ubuntu-Linux. With this release, Client VPN now supports the Windows Arm64 5.3.0. It already supports Mac OS version 13.0, 14.0 and 15.0, Windows 10 (x64) and Windows 11 (Arm64 and x64), and Ubuntu Linux 22.04 and 24.04 LTS versions.

This client version is available in all regions where AWS Client VPN is generally available with no additional cost.

To learn more about Client VPN:

• Visit the AWS Client VPN product page
• Read the AWS Client VPN documentation
• Read the AWS Client VPN user guide

​AWS Client VPN now supports Windows Arm64 client with version 5.3.0. You can now run the AWS supplied VPN client on the latest Windows Arm64 OS versions. AWS Client VPN desktop clients are available free of charge, and can be downloaded here. AWS Client VPN is a managed service that securely connects your remote workforce to AWS or on-premises networks. It supports desktop clients for MacOS, Windows x64, Windows Arm64 and Ubuntu-Linux. With this release, Client VPN now supports the Windows Arm64 5.3.0. It already supports Mac OS version 13.0, 14.0 and 15.0, Windows 10 (x64) and Windows 11 (Arm64 and x64), and Ubuntu Linux 22.04 and 24.04 LTS versions. This client version is available in all regions where AWS Client VPN is generally available with no additional cost. To learn more about Client VPN:

Visit the AWS Client VPN product page
Read the AWS Client VPN documentation
Read the AWS Client VPN user guide  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in the Asia Pacific (Osaka) Region. C7i instances are supported by custom Intel processors, available only on AWS, and offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers.

C7i instances deliver up to 15% better price-performance versus C6i instances and are a great choice for all compute-intensive workloads, such as batch processing, distributed analytics, ad-serving, and video encoding. C7i instances offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads.

C7i instances support new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. Customers can attach up to 128 EBS volumes to a C7i instance vs. up to 28 EBS volumes to a C6i instance. This allows processing of larger amounts of data, scale workloads, and improved performance over C6i instances.

To learn more, visit Amazon EC2 C7i Instances. To get started, see the AWS Management Console.

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in the Asia Pacific (Osaka) Region. C7i instances are supported by custom Intel processors, available only on AWS, and offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. C7i instances deliver up to 15% better price-performance versus C6i instances and are a great choice for all compute-intensive workloads, such as batch processing, distributed analytics, ad-serving, and video encoding. C7i instances offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads. C7i instances support new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. Customers can attach up to 128 EBS volumes to a C7i instance vs. up to 28 EBS volumes to a C6i instance. This allows processing of larger amounts of data, scale workloads, and improved performance over C6i instances. To learn more, visit Amazon EC2 C7i Instances. To get started, see the AWS Management Console.  

Today, SageMaker Training and Processing Jobs announces the new Amazon EC2 P5 instance size with one NVIDIA H100 GPU. It allows businesses to right-size their machine learning (ML) and high-performance computing (HPC) resources with cost-effectiveness. The new P5 instance type gives customers the flexibility to begin with smaller configurations and expand incrementally with fine-grained control, delivering enhanced cost management for their infrastructure investments.

P5.4xlarge instances are now available through SageMaker Flexible Training Plans in the following AWS Regions: US East (North Virginia, Ohio), US West (Oregon), Europe (London), Asia Pacific (Mumbai, Sydney, Tokyo), and South America (Sao Paulo). Additionally, the instance type can be purchased through SageMaker On-Demand and Spot in the Europe (London), Asia Pacific (Mumbai, Jakarta, Tokyo), and South America (Sao Paulo) AWS Regions.

To learn more about P5.4xlarge instances, visit Amazon EC2 P5 instances.

​Today, SageMaker Training and Processing Jobs announces the new Amazon EC2 P5 instance size with one NVIDIA H100 GPU. It allows businesses to right-size their machine learning (ML) and high-performance computing (HPC) resources with cost-effectiveness. The new P5 instance type gives customers the flexibility to begin with smaller configurations and expand incrementally with fine-grained control, delivering enhanced cost management for their infrastructure investments.
P5.4xlarge instances are now available through SageMaker Flexible Training Plans in the following AWS Regions: US East (North Virginia, Ohio), US West (Oregon), Europe (London), Asia Pacific (Mumbai, Sydney, Tokyo), and South America (Sao Paulo). Additionally, the instance type can be purchased through SageMaker On-Demand and Spot in the Europe (London), Asia Pacific (Mumbai, Jakarta, Tokyo), and South America (Sao Paulo) AWS Regions.
To learn more about P5.4xlarge instances, visit Amazon EC2 P5 instances.  

AWS Transfer Family Terraform module now supports deployment of SFTP connectors to transfer files between Amazon S3 and remote SFTP servers. This adds to the existing support for deploying SFTP server endpoints using Terraform, enabling you to automate and streamline centralized provisioning of file transfer resources using Infrastructure as Code (IaC).

SFTP connectors provide a fully managed and low-code capability to copy files between Amazon S3 and remote SFTP servers. You can now use Terraform to programmatically provision your SFTP connectors, associated dependencies and customizations in a single deployment. The module also provides end-to-end examples to automate file transfer workflows based on a schedule or event triggers. Using Terraform for deployment eliminates the need for time-consuming and error-prone manual configurations, and provides you a fast, repeatable and secure deployment option that can scale.

Customers can get started by downloading the Terraform module source code on GitHub. To learn more about Transfer Family, visit the product page and user guide. To see all the regions where Transfer Family is available, visit the AWS Region table.

​AWS Transfer Family Terraform module now supports deployment of SFTP connectors to transfer files between Amazon S3 and remote SFTP servers. This adds to the existing support for deploying SFTP server endpoints using Terraform, enabling you to automate and streamline centralized provisioning of file transfer resources using Infrastructure as Code (IaC). SFTP connectors provide a fully managed and low-code capability to copy files between Amazon S3 and remote SFTP servers. You can now use Terraform to programmatically provision your SFTP connectors, associated dependencies and customizations in a single deployment. The module also provides end-to-end examples to automate file transfer workflows based on a schedule or event triggers. Using Terraform for deployment eliminates the need for time-consuming and error-prone manual configurations, and provides you a fast, repeatable and secure deployment option that can scale. Customers can get started by downloading the Terraform module source code on GitHub. To learn more about Transfer Family, visit the product page and user guide. To see all the regions where Transfer Family is available, visit the AWS Region table.  

Amazon SageMaker HyperPod now supports customer managed AWS KMS keys (CMK) for encrypting EBS volumes, enabling enterprise customers to deploy machine learning clusters that meet their specific organizational security and compliance requirements. Customers training foundation models need full control over their encryption keys while maintaining high-performance computing capabilities, but previously could only rely on SageMaker HyperPod owned keys for cluster storage encryption.

This capability allows customers to encrypt both root and secondary EBS volumes using their own KMS keys, delivering enhanced security controls, regulatory compliance capabilities, and integration with existing key management workflows. The feature uses a grants-based approach for secure cross-account access and supports independent key selection for root and secondary volumes. You can specify customer managed KMS keys when creating or updating clusters using the CreateCluster and UpdateCluster APIs for clusters in continuous provisioning mode.

Customer managed KMS key support is available in all AWS Regions where SageMaker HyperPod is available. To learn more about customer managed key encryption for SageMaker HyperPod, see the user guide.

​Amazon SageMaker HyperPod now supports customer managed AWS KMS keys (CMK) for encrypting EBS volumes, enabling enterprise customers to deploy machine learning clusters that meet their specific organizational security and compliance requirements. Customers training foundation models need full control over their encryption keys while maintaining high-performance computing capabilities, but previously could only rely on SageMaker HyperPod owned keys for cluster storage encryption. This capability allows customers to encrypt both root and secondary EBS volumes using their own KMS keys, delivering enhanced security controls, regulatory compliance capabilities, and integration with existing key management workflows. The feature uses a grants-based approach for secure cross-account access and supports independent key selection for root and secondary volumes. You can specify customer managed KMS keys when creating or updating clusters using the CreateCluster and UpdateCluster APIs for clusters in continuous provisioning mode. Customer managed KMS key support is available in all AWS Regions where SageMaker HyperPod is available. To learn more about customer managed key encryption for SageMaker HyperPod, see the user guide.  

Amazon SageMaker HyperPod now supports the Amazon Elastic Block Store (EBS) Container Storage Interface (CSI) driver, enabling customers to dynamically provision and manage persistent storage for machine learning workloads on SageMaker HyperPod EKS clusters. This capability allows customers to create, attach, and manage EBS volumes through Kubernetes persistent volume claims, providing storage that persists across pod restarts and node replacements. Customers deploying training and inference workloads need flexible storage allocation while maintaining high performance, but previously required manual EBS volume management outside Kubernetes workflows.

EBS CSI driver support enables customers to dynamically provision volumes based on model requirements, resize volumes without service disruption, and create snapshots for backup and recovery. For training workloads, this provides persistent storage for datasets, model checkpoints, and shared artifacts. For inference workloads, customers can provision model storage, create caching volumes, and maintain event logging. The integration supports both static and dynamic provisioning through Kubernetes storage classes, optimizing storage costs and performance.

To get started, install the Amazon EBS CSI driver as an EKS add-on on your HyperPod EKS cluster, then provision EBS volumes using standard Kubernetes persistent volume claims and storage classes. The EBS CSI driver manages the complete lifecycle of EBS volumes, including creation, attachment, mounting, and cleanup. Volume encryption with customer-managed KMS keys is supported, and volumes can be resized and snapshotted through standard Kubernetes operations.

This feature is available in all AWS Regions where SageMaker HyperPod EKS clusters are supported. To learn more about EBS CSI driver support, see the Amazon SageMaker HyperPod User Guide.

​Amazon SageMaker HyperPod now supports the Amazon Elastic Block Store (EBS) Container Storage Interface (CSI) driver, enabling customers to dynamically provision and manage persistent storage for machine learning workloads on SageMaker HyperPod EKS clusters. This capability allows customers to create, attach, and manage EBS volumes through Kubernetes persistent volume claims, providing storage that persists across pod restarts and node replacements. Customers deploying training and inference workloads need flexible storage allocation while maintaining high performance, but previously required manual EBS volume management outside Kubernetes workflows. EBS CSI driver support enables customers to dynamically provision volumes based on model requirements, resize volumes without service disruption, and create snapshots for backup and recovery. For training workloads, this provides persistent storage for datasets, model checkpoints, and shared artifacts. For inference workloads, customers can provision model storage, create caching volumes, and maintain event logging. The integration supports both static and dynamic provisioning through Kubernetes storage classes, optimizing storage costs and performance. To get started, install the Amazon EBS CSI driver as an EKS add-on on your HyperPod EKS cluster, then provision EBS volumes using standard Kubernetes persistent volume claims and storage classes. The EBS CSI driver manages the complete lifecycle of EBS volumes, including creation, attachment, mounting, and cleanup. Volume encryption with customer-managed KMS keys is supported, and volumes can be resized and snapshotted through standard Kubernetes operations. This feature is available in all AWS Regions where SageMaker HyperPod EKS clusters are supported. To learn more about EBS CSI driver support, see the Amazon SageMaker HyperPod User Guide.