AWS Batch now provides enhanced visibility into your compute environments with two new capabilities that help you maintain operational best practices. When you describe a compute environment, you can now see the status of your Batch-provided default Amazon Machine Images (AMIs), indicating when updates are available. Additionally, AWS Batch now publishes AWS Health Planned Lifecycle Events to help you prepare for and track changes affecting your batch computing resources.

The AMI status indicator shows whether you’re using the latest AMI (LATEST) or if an update is available (UPDATE_AVAILABLE), helping you identify compute environments that may be running outdated AMIs. AWS Health Planned Lifecycle Events provide advance notification of upcoming changes, such as AMI deprecations, help you monitor migration status of your affected compute environments, and automate responses using Amazon EventBridge.

AMI status indicator and AWS Health Planned Lifecycle Events are available today in all AWS Regions where AWS Batch is available. For more information, see Managing AMI versions and AWS Health Planned Lifecycle Events pages in the AWS Batch User Guide.

​AWS Batch now provides enhanced visibility into your compute environments with two new capabilities that help you maintain operational best practices. When you describe a compute environment, you can now see the status of your Batch-provided default Amazon Machine Images (AMIs), indicating when updates are available. Additionally, AWS Batch now publishes AWS Health Planned Lifecycle Events to help you prepare for and track changes affecting your batch computing resources. The AMI status indicator shows whether you’re using the latest AMI (LATEST) or if an update is available (UPDATE_AVAILABLE), helping you identify compute environments that may be running outdated AMIs. AWS Health Planned Lifecycle Events provide advance notification of upcoming changes, such as AMI deprecations, help you monitor migration status of your affected compute environments, and automate responses using Amazon EventBridge. AMI status indicator and AWS Health Planned Lifecycle Events are available today in all AWS Regions where AWS Batch is available. For more information, see Managing AMI versions and AWS Health Planned Lifecycle Events pages in the AWS Batch User Guide.  

Amazon Bedrock AgentCore now enables customers to configure Chrome Enterprise policies for AgentCore Browser and specify custom root Certificate Authority (CA) certificates for both AgentCore Browser and Code Interpreter. These enhancements help ensure enterprise requirements are met when allowing AI agents to operate within organizations that have strict security policies and internal infrastructure using custom certificates.

With Chrome policies, you can leverage over 100+ configurable policies for managing browser behavior across security, URL filtering, content settings, and more to enforce organizational compliance requirements. For example, restrict agents to specific URLs for kiosk-mode operations, disable password managers and downloads for data-entry tasks, or implement URL blocklists for regulatory compliance. Custom root CA support enables agents to seamlessly connect to internal services like Artifactory, Jira, and finance portals that use SSL certificates signed by your organization’s internal Certificate Authority, and work with corporate proxies performing TLS interception.

These features are available in all 14 AWS Regions where Amazon Bedrock AgentCore Browser and Code Interpreter are available: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and Canada (Central).

To learn more, visit the AgentCore Browser documentation. 

​Amazon Bedrock AgentCore now enables customers to configure Chrome Enterprise policies for AgentCore Browser and specify custom root Certificate Authority (CA) certificates for both AgentCore Browser and Code Interpreter. These enhancements help ensure enterprise requirements are met when allowing AI agents to operate within organizations that have strict security policies and internal infrastructure using custom certificates. With Chrome policies, you can leverage over 100+ configurable policies for managing browser behavior across security, URL filtering, content settings, and more to enforce organizational compliance requirements. For example, restrict agents to specific URLs for kiosk-mode operations, disable password managers and downloads for data-entry tasks, or implement URL blocklists for regulatory compliance. Custom root CA support enables agents to seamlessly connect to internal services like Artifactory, Jira, and finance portals that use SSL certificates signed by your organization’s internal Certificate Authority, and work with corporate proxies performing TLS interception. These features are available in all 14 AWS Regions where Amazon Bedrock AgentCore Browser and Code Interpreter are available: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and Canada (Central). To learn more, visit the AgentCore Browser documentation.   

Amazon Route 53 Profiles now supports granular AWS Identity and Access Management (IAM) permissions, allowing you to control which users can manage specific resource types and VPC associations within your Profiles. With this launch, you can create IAM policies that restrict users to specific operations (associate, disassociate, or update) on individual resource types such as private hosted zones, Resolver rules, or DNS Firewall rule groups. You can also define permissions based on resource ARNs, hosted zone names, Resolver rule domain names, DNS Firewall rule group priority ranges, or specific VPC associations.

Route 53 Profiles enable you to define a standard DNS configuration that includes private hosted zone associations, Resolver rules, and DNS Firewall rule groups, and apply this configuration to multiple VPCs in your account or share with AWS accounts using AWS Resource Access Manager (RAM). This new capability provides administrators with fine-grained control over Profile management, enabling you to delegate specific responsibilities while maintaining security and governance standards across your organization.

This feature is available at no additional charge in all AWS Regions where Route 53 Profiles is available, except in Middle East (Bahrain) and Middle East (UAE). To learn more, see the Amazon Route 53 Profiles documentation and pricing page.

​Amazon Route 53 Profiles now supports granular AWS Identity and Access Management (IAM) permissions, allowing you to control which users can manage specific resource types and VPC associations within your Profiles. With this launch, you can create IAM policies that restrict users to specific operations (associate, disassociate, or update) on individual resource types such as private hosted zones, Resolver rules, or DNS Firewall rule groups. You can also define permissions based on resource ARNs, hosted zone names, Resolver rule domain names, DNS Firewall rule group priority ranges, or specific VPC associations. Route 53 Profiles enable you to define a standard DNS configuration that includes private hosted zone associations, Resolver rules, and DNS Firewall rule groups, and apply this configuration to multiple VPCs in your account or share with AWS accounts using AWS Resource Access Manager (RAM). This new capability provides administrators with fine-grained control over Profile management, enabling you to delegate specific responsibilities while maintaining security and governance standards across your organization. This feature is available at no additional charge in all AWS Regions where Route 53 Profiles is available, except in Middle East (Bahrain) and Middle East (UAE). To learn more, see the Amazon Route 53 Profiles documentation and pricing page.  

Amazon SageMaker AI now supports serverless model customization and reinforcement fine-tuning for 12 additional open-weight models, enabling you to fine-tune and evaluate them without provisioning or managing infrastructure. The newly supported models are: gpt-oss-120b, Qwen2.5 72B Instruct, DeepSeek-R1-Distill-Llama-70B, Qwen3 14B, DeepSeek-R1-Distill-Qwen-14B, Qwen2.5 14B Instruct, DeepSeek-R1-Distill-Llama-8B, DeepSeek-R1-Distill-Qwen-7B, Qwen3 4B, Meta Llama 3.2 3B Instruct, Qwen3 1.7B, and DeepSeek-R1-Distill-Qwen-1.5B. With this expansion, you can customize these models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques including RLVR and RLAIF, and only pay for what you use.

Reinforcement fine-tuning enables you to align models to complex, domain-specific reasoning tasks where techniques such as traditional SFT alone fall short. With RLVR, you can improve model accuracy on verifiable tasks such as code generation, math, and structured extraction by providing reward signals based on correctness. RLAIF uses AI-generated feedback to steer model behavior toward your quality and safety preferences. These techniques are available on previously supported and newly added models, with no cluster setup, capacity planning, or distributed training expertise required.

These models and fine-tuning techniques are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, see the Amazon SageMaker AI model customization product page and visit the Amazon SageMaker AI pricing page (Model Customization tab) to see the full list of models, techniques, and prices. 

​Amazon SageMaker AI now supports serverless model customization and reinforcement fine-tuning for 12 additional open-weight models, enabling you to fine-tune and evaluate them without provisioning or managing infrastructure. The newly supported models are: gpt-oss-120b, Qwen2.5 72B Instruct, DeepSeek-R1-Distill-Llama-70B, Qwen3 14B, DeepSeek-R1-Distill-Qwen-14B, Qwen2.5 14B Instruct, DeepSeek-R1-Distill-Llama-8B, DeepSeek-R1-Distill-Qwen-7B, Qwen3 4B, Meta Llama 3.2 3B Instruct, Qwen3 1.7B, and DeepSeek-R1-Distill-Qwen-1.5B. With this expansion, you can customize these models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques including RLVR and RLAIF, and only pay for what you use. Reinforcement fine-tuning enables you to align models to complex, domain-specific reasoning tasks where techniques such as traditional SFT alone fall short. With RLVR, you can improve model accuracy on verifiable tasks such as code generation, math, and structured extraction by providing reward signals based on correctness. RLAIF uses AI-generated feedback to steer model behavior toward your quality and safety preferences. These techniques are available on previously supported and newly added models, with no cluster setup, capacity planning, or distributed training expertise required. These models and fine-tuning techniques are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, see the Amazon SageMaker AI model customization product page and visit the Amazon SageMaker AI pricing page (Model Customization tab) to see the full list of models, techniques, and prices.   

AWS Batch now supports quota management with job preemption for SageMaker Training jobs, enabling you to efficiently allocate and share compute resources across your teams and projects. If you’re using GPU capacity in SageMaker Training jobs, you can now intelligently allocate compute resources, prioritize your business-critical training jobs, and automatically preempt lower-priority workloads when your urgent experiments arrive.

With quota management, you can create up to 20 quota shares per job queue that function as virtual queues with dedicated capacity limits and configurable resource sharing strategies. The service automatically uses cross-share preemption to restore borrowed capacity when the original owner submits jobs, and supports in-share preemption to allow high-priority jobs to preempt lower-priority jobs within the same quota share. You can monitor capacity utilization at the queue, quota share, and job-level granularity, update job priorities after submission to influence preemption decisions, and configure preemption retry limits to control behavior. The feature integrates directly with the SageMaker Python SDK via the aws_batch module.

Quota management with job preemption for SageMaker Training jobs is available today in all AWS Regions where AWS Batch is available. For more information, see our Quota Management example notebook on GitHub and the AWS Batch User Guide.

​AWS Batch now supports quota management with job preemption for SageMaker Training jobs, enabling you to efficiently allocate and share compute resources across your teams and projects. If you’re using GPU capacity in SageMaker Training jobs, you can now intelligently allocate compute resources, prioritize your business-critical training jobs, and automatically preempt lower-priority workloads when your urgent experiments arrive. With quota management, you can create up to 20 quota shares per job queue that function as virtual queues with dedicated capacity limits and configurable resource sharing strategies. The service automatically uses cross-share preemption to restore borrowed capacity when the original owner submits jobs, and supports in-share preemption to allow high-priority jobs to preempt lower-priority jobs within the same quota share. You can monitor capacity utilization at the queue, quota share, and job-level granularity, update job priorities after submission to influence preemption decisions, and configure preemption retry limits to control behavior. The feature integrates directly with the SageMaker Python SDK via the aws_batch module. Quota management with job preemption for SageMaker Training jobs is available today in all AWS Regions where AWS Batch is available. For more information, see our Quota Management example notebook on GitHub and the AWS Batch User Guide.  

Amazon Bedrock AgentCore Runtime now offers managed session storage in public preview, enabling agents to persist their filesystem state across stop and resume cycles. Modern agents write code, install packages, generate artifacts, and manage state through the filesystem. Until now, that work was lost when a session stopped. With managed session storage, everything your agent writes to a configured mount path persists automatically, even after the compute environment terminates.

When you configure session storage, each session gets a persistent directory at the mount path you specify. Your agent reads and writes files as normal, and AgentCore Runtime transparently replicates data to durable storage. When the session stops, data is flushed during graceful shutdown. When you resume with the same session ID, a new microVM mounts the same storage and the agent continues from where it left off — source files, installed packages, build artifacts, and git history all intact. No checkpoint logic, no save and restore code, and no changes to your agent application required. Session storage supports standard Linux filesystem operations including regular files, directories, and symlinks, with up to 1 GB per session and data retained for 14 days of idle time. Storage communication is confined to a single session’s data and cannot access other sessions or AgentCore Runtime environments.

Session storage is available in public preview across fourteen AWS Regions: US (N. Virginia, Ohio, Oregon), Canada (Central), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, London, Paris, Stockholm).

To learn more, see persist files across stop/resume in the Amazon Bedrock AgentCore documentation.

​Amazon Bedrock AgentCore Runtime now offers managed session storage in public preview, enabling agents to persist their filesystem state across stop and resume cycles. Modern agents write code, install packages, generate artifacts, and manage state through the filesystem. Until now, that work was lost when a session stopped. With managed session storage, everything your agent writes to a configured mount path persists automatically, even after the compute environment terminates.
When you configure session storage, each session gets a persistent directory at the mount path you specify. Your agent reads and writes files as normal, and AgentCore Runtime transparently replicates data to durable storage. When the session stops, data is flushed during graceful shutdown. When you resume with the same session ID, a new microVM mounts the same storage and the agent continues from where it left off — source files, installed packages, build artifacts, and git history all intact. No checkpoint logic, no save and restore code, and no changes to your agent application required. Session storage supports standard Linux filesystem operations including regular files, directories, and symlinks, with up to 1 GB per session and data retained for 14 days of idle time. Storage communication is confined to a single session’s data and cannot access other sessions or AgentCore Runtime environments.
Session storage is available in public preview across fourteen AWS Regions: US (N. Virginia, Ohio, Oregon), Canada (Central), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, London, Paris, Stockholm).
To learn more, see persist files across stop/resume in the Amazon Bedrock AgentCore documentation.  

AWS Backup now supports Amazon DocumentDB in 12 additional AWS Regions: Asia Pacific (Malaysia, Thailand, Osaka, Hong Kong, Jakarta, Melbourne), Europe (Stockholm, Spain, Zurich), Africa (Cape Town), Israel (Tel Aviv), and Mexico (Central).

This expansion brings policy-based data protection and recovery to your Amazon DocumentDB clusters in these newly supported Regions.

To start protecting your DocumentDB clusters with AWS Backup, add your DocumentDB clusters to your existing backup plans, or create a new backup plan and attach your DocumentDB clusters to it. To learn more about AWS Backup for Amazon DocumentDB, visit the product page, pricing page, and documentation. To get started, visit the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs.

​AWS Backup now supports Amazon DocumentDB in 12 additional AWS Regions: Asia Pacific (Malaysia, Thailand, Osaka, Hong Kong, Jakarta, Melbourne), Europe (Stockholm, Spain, Zurich), Africa (Cape Town), Israel (Tel Aviv), and Mexico (Central).
This expansion brings policy-based data protection and recovery to your Amazon DocumentDB clusters in these newly supported Regions.
To start protecting your DocumentDB clusters with AWS Backup, add your DocumentDB clusters to your existing backup plans, or create a new backup plan and attach your DocumentDB clusters to it. To learn more about AWS Backup for Amazon DocumentDB, visit the product page, pricing page, and documentation. To get started, visit the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs.  

• Categoría: Noticias de Microsoft

marzo 25, 2026

Empoderar al sector sin ánimo de lucro para afrontar los retos del mañana

Por: Justin Spelhaug, presidente, Microsoft Elevate.

Al dar la bienvenida a más de 1.500 líderes de organizaciones sin ánimo de lucro de todo el mundo a la Cumbre Global de Líderes de Organizaciones Sin Ánimo de Lucro de Microsoft, recordamos el extraordinario trabajo que realizan cada día las organizaciones para fortalecer comunidades y ampliar las oportunidades. La reunión de hoy llega en un momento crucial, ya que las organizaciones sin ánimo de lucro se enfrentan a un nuevo conjunto de preguntas: ¿Cómo puede la IA ayudarles a servir a sus comunidades de manera más eficaz? ¿Y cómo desarrollan las habilidades y la capacidad para liderar este cambio desde dentro?

Las grandes transiciones tecnológicas rara vez se desarrollan de manera uniforme. A medida que la IA se difunde entre economías y sectores, crea nuevas oportunidades, pero también introduce una disrupción significativa para trabajadores, familias y comunidades. Las organizaciones sin ánimo de lucro están en una posición única para impulsar cambios significativos en el mundo actual. Son las organizaciones a las que la gente recurre primero para apoyar a quienes desarrollan nuevas habilidades, encuentran nuevas vías hacia el empleo y se mantienen conectados con los sistemas que les sostienen.

Para ayudarles a maximizar su impacto con mayor escala y eficiencia, hoy anunciamos Microsoft Elevate for Changemakers, una nueva iniciativa que proporciona a los líderes de organizaciones sin ánimo de lucro credenciales esenciales en IA, acceso a una comunidad de compañeros sólida y recursos de desarrollo de capacidades basados en roles. Este programa está diseñado para empoderar a quienes están a la vanguardia de los desafíos sociales para que lideren la adopción de la IA con confianza, de manera que refleje sus misiones y las comunidades a las que sirven.

Este nuevo programa forma parte de nuestro compromiso más amplio con Microsoft Elevate para garantizar que las personas puedan prosperar en la economía de la IA y refleja el legado de 50 años de Microsoft, en apoyo a organizaciones sin ánimo de lucro. Como organización, estamos orgullosos de colaborar con casi un millón de organizaciones sin ánimo de lucro y sistemas educativos en todo el mundo, y solo en el próximo año entregaremos más de 5.000 millones de dólares en descuentos, donaciones y subvenciones para ayudar a organizaciones sin ánimo de lucro a atender las necesidades de la comunidad.

Respaldado por el compromiso de Microsoft de garantizar que todos tengan oportunidades en la era de la IA, Microsoft Elevate for Changemakers ayuda a garantizar que quienes trabajan más cerca de los desafíos comunitarios sigan a la vanguardia de las soluciones impulsadas por IA.

El nuevo programa Microsoft Elevate for Changemakers incluye:

• Credencial de IA para organizaciones sin ánimo de lucro: La credencial de IA para organizaciones sin ánimo de lucro es un certificado profesional, desarrollado con LinkedIn y NetHope, que ofrece a los participantes un camino de aprendizaje claro y estructurado, centrado en el trabajo real que se realiza en el sector sin ánimo de lucro. Los beneficiarios reciben un certificado profesional de LinkedIn, que reconoce de manera formal su creciente experiencia y su compromiso con el uso responsable de la IA en sus organizaciones.
• Formación en IA en vivo y bajo demanda para desarrollar capacidad: Formación práctica en habilidades basadas en el trabajo real de organizaciones sin ánimo de lucro, no en contenido genérico de IA reempaquetado para el sector. Desde los fundamentos de Copilot hasta la gestión del cambio y la gobernanza responsable de la IA, cada módulo está diseñado para simplificar los flujos de trabajo de las organizaciones sin ánimo de lucro y ayudarles a hacer más con facilidad.
• Una Beca Changemaker, un programa global para profesionales del sector sin ánimo de lucro en organizaciones con proyectos de IA accionables listos para avanzar en sus misiones. Esta beca proporciona los recursos esenciales, la inversión y la orientación experta necesarios para convertir la ambición de la IA en un impacto duradero. Los becarios se unirán a una cohorte mundial, crearán e implementarán planes responsables de adopción de IA, desarrollarán habilidades técnicas y de gestión del cambio críticas, y se conectarán con una red de confianza de líderes de IA sin ánimo de lucro, todo ello con el apoyo de Microsoft y socios de lanzamiento, incluyendo EY y Caribou. La Beca Changemaker está ahora abierta a organizaciones sin ánimo de lucro de todos los procedentes. Quienes estén dispuestos a marcar la diferencia con la IA pueden manifestar su interés hoy en https://aka.ms/MicrosoftElevateforChangemakers

Transformar posibilidades al empoderar a las organizaciones sin ánimo de lucro

En su mejor momento, la IA debería ampliar la agencia humana en lugar de reemplazarla. La verdadera oportunidad es dar a las personas más capacidad para resolver problemas, construir nuevas ideas y fortalecer las comunidades que les rodean.

En todo el sector sin ánimo de lucro, esto ha comenzado a tomar forma de manera práctica:

• Tiempo enriquecedor del personal. Gran parte de la semana típica de empleados de una organización sin ánimo de lucro no contribuye de manera directa al trabajo misionero. De hecho, investigaciones revelan que casi la mitad de las organizaciones sin ánimo de lucro todavía utilizan la introducción manual de datos y hojas de cálculo para la documentación de cumplimiento, resúmenes de reuniones, notas de casos y otras operaciones. La IA reduce esa carga de formas que ya son reales y medibles, para devolver a las personas más de su semana por el trabajo que vinieron a hacer. ARCare, un proveedor sanitario en comunidades desfavorecidas de Arkansas, Kentucky y Mississippi, ya ha comenzado a ver los beneficios de su uso de tecnología de IA. Con la IA encargándose de las tareas administrativas, el personal dedica menos tiempo a la recogida de datos y más tiempo a la atención al paciente, y estiman que han eliminado entre 6 y 8 horas diarias de tareas manuales.
• Ofrecer programas más impactantes. La IA da a las organizaciones sin ánimo de lucro la capacidad de escalar lo que funciona sin perder lo que lo hace funcionar. Opportunity International utiliza la IA para escalar el impacto a través de un chatbot en idioma local, con el fin de ofrecer a los agricultores orientación agrícola instantánea, para superar barreras de alfabetización y ampliar de manera importante su alcance. Al hacer accesible el conocimiento crítico, la IA libera a los equipos de primera línea para que se centren en las relaciones, la mentoría y el cambio a largo plazo que los programas tradicionales por sí solos no pueden lograr.
• Involucrar a los simpatizantes y financiadores de manera más eficaz. Recaudar fondos, obtener subvenciones y construir relaciones con donantes suelen ser las prioridades estratégicas más importantes —y los retos— para las organizaciones sin ánimo de lucro que enfrentan una demanda creciente de servicios en un momento de incertidumbre económica. La IA no reemplaza las relaciones con los donantes. Da a las personas que gestionan esas relaciones más tiempo y capacidad para centrarse en lo que las construye. Head Start Homes descubrió que, a medida que la IA aumentaba su capacidad organizativa, podían escalar programas y atraer nueva financiación.
• Operaciones de transformación. La IA otorga a las organizaciones sin ánimo de lucro la capacidad de innovar y optimizar su forma de trabajar, para aportar mayor seguridad, datos más precisos y una toma de decisiones más informada a todas las partes de la organización. El resultado es menos tiempo dedicado a gestionar la complejidad y más capacidad dirigida hacia los resultados. La organización de vivienda social, de Alliantie, es un buen ejemplo. El uso de la IA ha permitido a de Alliantie aumentar la eficiencia a través de mantener un enfoque centrado en el ser humano para el apoyo a la vivienda en el centro de todo lo que hacen. Con más de 3.000 llamadas cada semana para apoyo a la vivienda, el uso de un chatbot de IA permitió al personal del centro de llamadas ayudar a más personas, porque el objetivo nunca fue la eficiencia por sí misma. Era para asegurarse de que el beneficio humano siempre fuera lo primero.

Estas historias reales de la IA que empodera a organizaciones con misión son posibles gracias a personas dedicadas dentro de organizaciones sin ánimo de lucro que han dado un paso adelante para liderar cambios transformadores, a menudo sin reconocimiento formal ni mandato oficial. Es su disposición a adoptar nuevas tecnologías, aprender nuevas habilidades y promover la adopción responsable de la IA lo que impulsa el sector hacia adelante.

El trabajo que viene

Los millones de agentes de cambio, voluntarios y líderes globales del sector sin ánimo de lucro redefinen lo que es posible, al asegurar que la IA sirva como herramienta para amplificar la capacidad y el propósito humanos, en lugar de reemplazarlos. Su compromiso y liderazgo son la fuerza motriz detrás de un futuro en el que las organizaciones sin ánimo de lucro aprovechen la IA para lograr un mayor impacto, profundizar las relaciones y fortalecer comunidades.

El camino por seguir estará marcado por la fortaleza y el liderazgo del sector sin ánimo de lucro. Cada día, estas organizaciones demuestran lo que significa permanecer cerca de las comunidades, responder en momentos de cambio y ayudar a las personas a afrontar la incertidumbre con confianza y coherencia.

Eso es lo que hace que este momento sea diferente. A medida que la IA se adopta cada vez más, las organizaciones mejor posicionadas para asegurar que sus beneficios se compartan de manera amplia son las que ya realizan este trabajo.

En Microsoft, construimos sobre una tradición de apoyo a este sector que comenzó hace cinco décadas con nuestro fundador y continúa hoy en día. El nuestro es un compromiso a largo plazo. No solo como socio tecnológico, sino como parte de un esfuerzo más amplio para ayudar a garantizar que los beneficios de la IA lleguen a las comunidades que sirven las organizaciones sin ánimo de lucro. Seguiremos con las inversiones en la capacidad, herramientas y alianzas que apoyan este trabajo y esperamos construir juntos lo que venga a continuación.

Etiquetas:

• IA
• On the Issues
• Tecnología

The post Empoderar al sector sin ánimo de lucro para afrontar los retos del mañana appeared first on Source LATAM.

​The post Empoderar al sector sin ánimo de lucro para afrontar los retos del mañana appeared first on Source LATAM.  

AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hong Kong), Asia Pacific (Seoul), Asia Pacific (Melbourne), Asia Pacific (Thailand), Europe (Zurich), Europe (Milan) and Mexico (Central) regions. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance versus I3en instances.

I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances.

I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS).

To learn more, visit the I7ie instances page.

​AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hong Kong), Asia Pacific (Seoul), Asia Pacific (Melbourne), Asia Pacific (Thailand), Europe (Zurich), Europe (Milan) and Mexico (Central) regions. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance versus I3en instances.
I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances.
I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS).
To learn more, visit the I7ie instances page.  

Amazon SageMaker HyperPod now extends continuous provisioning support to clusters using the Slurm orchestrator, enabling greater flexibility and efficiency for enterprise customers running large-scale AI/ML training workloads. AI/ML customers running Slurm-based clusters need to start training quickly, scale seamlessly, perform maintenance without disrupting operations, and have granular visibility into cluster operations. Previously, if any instance group could not be fully provisioned, the entire cluster creation or scaling operation failed and rolled back, causing delays and requiring manual intervention.

With continuous provisioning for Slurm, SageMaker HyperPod automatically provisions remaining capacity in the background while training jobs can begin immediately on available instances. The system uses priority-based provisioning to bring up the Slurm controller node first, followed by login and worker nodes in parallel, so your cluster reaches an operational state as quickly as possible. HyperPod retries failed node launches asynchronously and adds nodes to the Slurm cluster automatically as they become available, ensuring clusters reliably reach their desired scale without requiring manual intervention. You can now perform concurrent, non-blocking scaling operations across multiple instance groups simultaneously — a capacity shortage in one instance group no longer blocks scaling in others. These capabilities help customers reduce time-to-training, maximize resource utilization, and focus on innovation rather than infrastructure management.

This feature is available for new SageMaker HyperPod clusters using the Slurm orchestrator. You can enable continuous provisioning by setting the NodeProvisioningMode parameter to «Continuous» when creating new HyperPod clusters using the CreateCluster API. Continuous provisioning can also be enabled when creating new clusters through the AWS CLI and the SageMaker AI console.

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

​Amazon SageMaker HyperPod now extends continuous provisioning support to clusters using the Slurm orchestrator, enabling greater flexibility and efficiency for enterprise customers running large-scale AI/ML training workloads. AI/ML customers running Slurm-based clusters need to start training quickly, scale seamlessly, perform maintenance without disrupting operations, and have granular visibility into cluster operations. Previously, if any instance group could not be fully provisioned, the entire cluster creation or scaling operation failed and rolled back, causing delays and requiring manual intervention. With continuous provisioning for Slurm, SageMaker HyperPod automatically provisions remaining capacity in the background while training jobs can begin immediately on available instances. The system uses priority-based provisioning to bring up the Slurm controller node first, followed by login and worker nodes in parallel, so your cluster reaches an operational state as quickly as possible. HyperPod retries failed node launches asynchronously and adds nodes to the Slurm cluster automatically as they become available, ensuring clusters reliably reach their desired scale without requiring manual intervention. You can now perform concurrent, non-blocking scaling operations across multiple instance groups simultaneously — a capacity shortage in one instance group no longer blocks scaling in others. These capabilities help customers reduce time-to-training, maximize resource utilization, and focus on innovation rather than infrastructure management. This feature is available for new SageMaker HyperPod clusters using the Slurm orchestrator. You can enable continuous provisioning by setting the NodeProvisioningMode parameter to «Continuous» when creating new HyperPod clusters using the CreateCluster API. Continuous provisioning can also be enabled when creating new clusters through the AWS CLI and the SageMaker AI console. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about continuous provisioning for Slurm clusters, see the Amazon SageMaker HyperPod User Guide.