AWS Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects for films, television, broadcasting, web content, and design. Today, we’re excited to announce a new wait and save feature for Deadline Cloud service-managed fleets that can reduce rendering costs with prices starting as low as $0.006 per vCPU-hour.
This new feature is ideal for non time-sensitive rendering workloads with flexible completion times. Submitting jobs using this wait and save approach allows you to achieve significant cost savings so you can do more creative iteration and exploration on your next project. This feature complements existing AWS Deadline Cloud compute options in its service-managed fleets, giving you more flexibility to optimize your resource utilization across different priorities and budgets.
AWS Deadline Cloud wait and save is available in all AWS Regions where AWS Deadline Cloud is offered. To learn more about this new cost-saving feature and how it can help optimize your rendering workloads, visit the AWS Deadline Cloud product page or review the AWS Deadline Cloud documentation.
AWS Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects for films, television, broadcasting, web content, and design. Today, we’re excited to announce a new wait and save feature for Deadline Cloud service-managed fleets that can reduce rendering costs with prices starting as low as $0.006 per vCPU-hour. This new feature is ideal for non time-sensitive rendering workloads with flexible completion times. Submitting jobs using this wait and save approach allows you to achieve significant cost savings so you can do more creative iteration and exploration on your next project. This feature complements existing AWS Deadline Cloud compute options in its service-managed fleets, giving you more flexibility to optimize your resource utilization across different priorities and budgets. AWS Deadline Cloud wait and save is available in all AWS Regions where AWS Deadline Cloud is offered. To learn more about this new cost-saving feature and how it can help optimize your rendering workloads, visit the AWS Deadline Cloud product page or review the AWS Deadline Cloud documentation.
Today, Amazon SageMaker AI announces support for P6e-GB200 UltraServers in SageMaker HyperPod and Training Jobs. With P6e-GB200 UltraServers, you can leverage up to 72 NVIDIA Blackwell GPUs under one NVLink domain to accelerate training and deployment of foundational models at trillion-parameter scale. P6e-GB200 UltraServers are available in two sizes: ml.u-p6e-gb200x72 (72 GPUs within NVLink) and ml.u-p6e-gb200x36 (36 GPUs within NVLink).
P6e-GB200 UltraServers deliver over 20x compute and over 11x memory under one NVIDIA NVLink compared to P5en instances. Within each NVLink domain you can leverage 360 petaflops of FP8 compute (without sparsity) and 13.4 TB of total high bandwidth memory (HBM3e). When you use P6e-GB200 UltraServers on SageMaker AI, you get the GB200’s superior performance combined with SageMaker’s managed infrastructure such as security, built-in fault tolerance, topology aware scheduling (SageMaker HyperPod EKS & Slurm), integrated monitoring capabilities, and native integration with other SageMaker AI and AWS services.
The UltraServers are available through SageMaker Flexible Training Plans in the Dallas Local Zone («us-east-1-dfw-2a»), an extension of the US East (N. Virginia) AWS Region. For on-demand reservation of GB200 UltraServers, please reach out to your account manager. Amazon SageMaker AI lets you easily train and deploy machine learning models at scale using fully managed infrastructure optimized for performance and cost. To get started with UltraServers on SageMaker AI, visit the documentation.
Today, Amazon SageMaker AI announces support for P6e-GB200 UltraServers in SageMaker HyperPod and Training Jobs. With P6e-GB200 UltraServers, you can leverage up to 72 NVIDIA Blackwell GPUs under one NVLink domain to accelerate training and deployment of foundational models at trillion-parameter scale. P6e-GB200 UltraServers are available in two sizes: ml.u-p6e-gb200x72 (72 GPUs within NVLink) and ml.u-p6e-gb200x36 (36 GPUs within NVLink). P6e-GB200 UltraServers deliver over 20x compute and over 11x memory under one NVIDIA NVLink compared to P5en instances. Within each NVLink domain you can leverage 360 petaflops of FP8 compute (without sparsity) and 13.4 TB of total high bandwidth memory (HBM3e). When you use P6e-GB200 UltraServers on SageMaker AI, you get the GB200’s superior performance combined with SageMaker’s managed infrastructure such as security, built-in fault tolerance, topology aware scheduling (SageMaker HyperPod EKS & Slurm), integrated monitoring capabilities, and native integration with other SageMaker AI and AWS services. The UltraServers are available through SageMaker Flexible Training Plans in the Dallas Local Zone («us-east-1-dfw-2a»), an extension of the US East (N. Virginia) AWS Region. For on-demand reservation of GB200 UltraServers, please reach out to your account manager. Amazon SageMaker AI lets you easily train and deploy machine learning models at scale using fully managed infrastructure optimized for performance and cost. To get started with UltraServers on SageMaker AI, visit the documentation.
Amazon Web Services, Inc. (AWS) announces the launch of the AWS Marketplace Private Offer Promotion Program (MPOPP) in AWS Partner Central to support independent software vendors (ISVs) with driving new customer acquisition. This program is designed to accelerate sales through AWS Marketplace by offering AWS Promotional Credits to customers as an incentive for purchasing from participating ISVs. MPOPP offers benefits for AWS Partners at different stages in their AWS Marketplace journey. New AWS Marketplace Sellers can benefit from immediate funding support, and established sellers can benefit from special incentives for driving AWS Marketplace renewals.
Eligible Partners can submit self-service requests for funds through the AWS Partner Funding Portal year-round, enabling funding to be targeted for next business day delivery following Private Offer acceptance. The simplified funding template can help accelerate deal closure and provides better speed-to-market with a fully automated approval process. Following deal completion and AWS Marketplace transaction verification, Promotional Credits will be issued to the customer’s AWS account based on the Total Contract Value (TCV) and applicable program rates, streamlining the entire process from planning to credit disbursement.
Amazon Web Services, Inc. (AWS) announces the launch of the AWS Marketplace Private Offer Promotion Program (MPOPP) in AWS Partner Central to support independent software vendors (ISVs) with driving new customer acquisition. This program is designed to accelerate sales through AWS Marketplace by offering AWS Promotional Credits to customers as an incentive for purchasing from participating ISVs. MPOPP offers benefits for AWS Partners at different stages in their AWS Marketplace journey. New AWS Marketplace Sellers can benefit from immediate funding support, and established sellers can benefit from special incentives for driving AWS Marketplace renewals. Eligible Partners can submit self-service requests for funds through the AWS Partner Funding Portal year-round, enabling funding to be targeted for next business day delivery following Private Offer acceptance. The simplified funding template can help accelerate deal closure and provides better speed-to-market with a fully automated approval process. Following deal completion and AWS Marketplace transaction verification, Promotional Credits will be issued to the customer’s AWS account based on the Total Contract Value (TCV) and applicable program rates, streamlining the entire process from planning to credit disbursement. To learn more about the MPOPP, eligibility, and benefits, visit the AWS Partner Funding Benefits Guide (AWS Partner Central login required).
Amazon SageMaker HyperPod now supports custom AMIs, enabling customers to deploy clusters with pre-configured, security-hardened environments that meet their specific organizational requirements. Customers deploying AI/ML workloads on HyperPod need customized environments that meet strict security, compliance, and operational requirements while maintaining fast cluster startup times, but often struggle with complex lifecycle configuration scripts that slow deployment and create inconsistencies across cluster nodes.
This capability allows customers to build upon HyperPod’s performance-optimized base AMIs while incorporating customized security agents, compliance tools, proprietary libraries, and specialized drivers directly into the image, delivering faster startup times, improved reliability, and enhanced security compliance. Security teams can embed organizational policies directly into base images, allowing AI/ML teams to use pre-approved environments that accelerate time-to-training while meeting enterprise security standards. You can specify custom AMIs when creating new HyperPod clusters using the CreateCluster API, adding instance groups with UpdateCluster API, or patching existing clusters with UpdateClusterSoftware API. Custom AMIs must be built using HyperPod’s public base AMIs to maintain compatibility with distributed training libraries and cluster management capabilities.
This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about custom AMI support, see the Amazon SageMaker HyperPod User Guide.
Amazon SageMaker HyperPod now supports custom AMIs, enabling customers to deploy clusters with pre-configured, security-hardened environments that meet their specific organizational requirements. Customers deploying AI/ML workloads on HyperPod need customized environments that meet strict security, compliance, and operational requirements while maintaining fast cluster startup times, but often struggle with complex lifecycle configuration scripts that slow deployment and create inconsistencies across cluster nodes. This capability allows customers to build upon HyperPod’s performance-optimized base AMIs while incorporating customized security agents, compliance tools, proprietary libraries, and specialized drivers directly into the image, delivering faster startup times, improved reliability, and enhanced security compliance. Security teams can embed organizational policies directly into base images, allowing AI/ML teams to use pre-approved environments that accelerate time-to-training while meeting enterprise security standards. You can specify custom AMIs when creating new HyperPod clusters using the CreateCluster API, adding instance groups with UpdateCluster API, or patching existing clusters with UpdateClusterSoftware API. Custom AMIs must be built using HyperPod’s public base AMIs to maintain compatibility with distributed training libraries and cluster management capabilities. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about custom AMI support, see the Amazon SageMaker HyperPod User Guide.
Today, AWS announced the expansion of 100 Gbps dedicated connections at the AWS Direct Connect location in the Teraco CT1 data center near Cape Town, South Africa. You can now establish private, direct network access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones from this location. This is the second AWS Direct Connect location in South Africa to provide 100 Gbps connections with MACsec encryption capabilities.
The Direct Connect service enables you to establish a private, physical network connection between AWS and your data center, office, or colocation environment. These private connections can provide a more consistent network experience than those made over the public internet.
For more information on the over 142 Direct Connect locations worldwide, visit the locations section of the Direct Connect product detail pages. Or, visit our getting started page to learn more about how to purchase and deploy Direct Connect.
Today, AWS announced the expansion of 100 Gbps dedicated connections at the AWS Direct Connect location in the Teraco CT1 data center near Cape Town, South Africa. You can now establish private, direct network access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones from this location. This is the second AWS Direct Connect location in South Africa to provide 100 Gbps connections with MACsec encryption capabilities.
The Direct Connect service enables you to establish a private, physical network connection between AWS and your data center, office, or colocation environment. These private connections can provide a more consistent network experience than those made over the public internet.
For more information on the over 142 Direct Connect locations worldwide, visit the locations section of the Direct Connect product detail pages. Or, visit our getting started page to learn more about how to purchase and deploy Direct Connect.
Today, we are introducing multiple enhancements to Amazon EC2 On-Demand Capacity Reservations in Cluster Placement Groups (CPG-ODCRs). CPG-ODCRs provide customers with assured capacity and offer low latency and high throughput between instances within the same Cluster Placement Group (CPG). Now, customers using CPG-ODCRs can benefit from two additional capabilities that make them easier to use. First, customers can now add ODCRs belonging to different CPGs to Resource Groups which will enable customers to manage and target groups of reservations spread across multiple Placement Groups. Second, customers can share CPG-ODCRs across multiple AWS accounts through AWS Resource Access Manager, which allow them to create central pools of capacity and use them efficiently across workloads in different accounts.
Customers can get started with these capabilities of CPG-ODCR by using the AWS CLI/APIs or by visiting the AWS Management console. These capabilities are now available in all AWS regions except China, and they are available at no additional cost. To learn more about these capabilities, please refer to the Capacity Reservations user guide.
Today, we are introducing multiple enhancements to Amazon EC2 On-Demand Capacity Reservations in Cluster Placement Groups (CPG-ODCRs). CPG-ODCRs provide customers with assured capacity and offer low latency and high throughput between instances within the same Cluster Placement Group (CPG). Now, customers using CPG-ODCRs can benefit from two additional capabilities that make them easier to use. First, customers can now add ODCRs belonging to different CPGs to Resource Groups which will enable customers to manage and target groups of reservations spread across multiple Placement Groups. Second, customers can share CPG-ODCRs across multiple AWS accounts through AWS Resource Access Manager, which allow them to create central pools of capacity and use them efficiently across workloads in different accounts. Customers can get started with these capabilities of CPG-ODCR by using the AWS CLI/APIs or by visiting the AWS Management console. These capabilities are now available in all AWS regions except China, and they are available at no additional cost. To learn more about these capabilities, please refer to the Capacity Reservations user guide.
Anthropic’s Claude Sonnet 4 in Amazon Bedrock is launching today with a significantly expanded context window in public preview. The context window has been increased from 200,000 to 1 million tokens, representing a 5x expansion. This enhancement allows Claude to process and reason over much larger amounts of text in a single request, opening up new possibilities for comprehensive analysis and generation tasks.
This expanded context window for Sonnet 4 brings many benefits to customers. For large-scale code analysis, users can now load entire codebases, including source files, tests, and documentation, enabling Sonnet 4 to understand project architecture, identify cross-file dependencies, and suggest improvements that account for the complete system design. In document synthesis, the model can now process extensive document sets like legal contracts, lengthy research papers, large datasets, or technical specifications in a single API call, analyzing relationships across hundreds of documents while maintaining full context. Additionally, this expansion allows for the creation of more sophisticated context-aware agents that can maintain coherence across hundreds of tool calls and multi-step workflows, including complete API documentation and interaction histories.
The expanded context window for Claude Sonnet 4 is now available in public preview in Amazon Bedrock in US West (Oregon), US East (N. Virginia), and US East (Ohio) AWS regions. Prompts over 200,000 tokens will incur approximately twice the token price for input and 1.5 times for output. To get started with the expanded context window for Claude Sonnet 4, visit the Amazon Bedrock console or refer to the Amazon Bedrock documentation.
Anthropic’s Claude Sonnet 4 in Amazon Bedrock is launching today with a significantly expanded context window in public preview. The context window has been increased from 200,000 to 1 million tokens, representing a 5x expansion. This enhancement allows Claude to process and reason over much larger amounts of text in a single request, opening up new possibilities for comprehensive analysis and generation tasks. This expanded context window for Sonnet 4 brings many benefits to customers. For large-scale code analysis, users can now load entire codebases, including source files, tests, and documentation, enabling Sonnet 4 to understand project architecture, identify cross-file dependencies, and suggest improvements that account for the complete system design. In document synthesis, the model can now process extensive document sets like legal contracts, lengthy research papers, large datasets, or technical specifications in a single API call, analyzing relationships across hundreds of documents while maintaining full context. Additionally, this expansion allows for the creation of more sophisticated context-aware agents that can maintain coherence across hundreds of tool calls and multi-step workflows, including complete API documentation and interaction histories. The expanded context window for Claude Sonnet 4 is now available in public preview in Amazon Bedrock in US West (Oregon), US East (N. Virginia), and US East (Ohio) AWS regions. Prompts over 200,000 tokens will incur approximately twice the token price for input and 1.5 times for output. To get started with the expanded context window for Claude Sonnet 4, visit the Amazon Bedrock console or refer to the Amazon Bedrock documentation.
Amazon OpenSearch Serverless has introduced several new features including kNN Byte vector support, radial search capabilities for Vector collections, and new data types and mapping parameters such as strict_allow_templates, wildcard field type, and kuromoji_completion analyzer.
These enhancements deliver significant benefits for search and analytics workloads. The kNN Byte vector support helps reduce costs through lower memory and storage requirements while improving latency and performance. The additional features like nested fields for storing multiple vectors in a single document and new mapping parameters provide greater flexibility and control in managing search operations without the complexity of infrastructure management.
Amazon OpenSearch Serverless has introduced several new features including kNN Byte vector support, radial search capabilities for Vector collections, and new data types and mapping parameters such as strict_allow_templates, wildcard field type, and kuromoji_completion analyzer. These enhancements deliver significant benefits for search and analytics workloads. The kNN Byte vector support helps reduce costs through lower memory and storage requirements while improving latency and performance. The additional features like nested fields for storing multiple vectors in a single document and new mapping parameters provide greater flexibility and control in managing search operations without the complexity of infrastructure management. 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.
Descubran el potencial de la IA agéntica en la educación superior
Por: Equipo de Microsoft Educación.
En Microsoft Build 2025, presentamos una nueva ola de innovaciones agénticas que han comenzado a remodelar la forma en que las instituciones de educación superior usan la IA. Desde agentes inteligentes hasta plataformas de datos unificadas, estos avances permiten a los líderes de la educación superior acelerar con confianza la transformación digital. Una parte clave de esta evolución es el papel de Azure AI Foundry en la educación, que ayuda a las instituciones a crear soluciones de IA seguras y escalables adaptadas a sus objetivos académicos.
Con estos avances en IA, las instituciones ahora tienen una nueva y poderosa oportunidad: utilizar agentes que puedan automatizar tareas rutinarias, ayudar a la facultad y al personal, y proporcionar información contextual en tiempo real para respaldar la enseñanza y el aprendizaje. A medida que más instituciones comiencen su recorrido hacia esta próxima frontera, los agentes de IA apoyarán a las personas y los equipos en la automatización de las tareas y en brindar información contextual instantánea.
Esto significa que con las herramientas de IA centradas en datos de Microsoft, su institución puede:
Desarrollar agentes escalables e inteligentes con Azure AI Foundry, en los que confían las empresas y personalizados para el cumplimiento y la innovación de la educación superior.
Convertir la información de los datos en acción con análisis impulsados por IA, para abordar los desafíos en el éxito de los estudiantes, la productividad de la investigación y la agilidad operativa.
Acelerar la inteligencia artificial agentica con Azure AI Foundry
Azure AI Foundry Agent Service permite a las instituciones diseñar, implementar y escalar agentes de forma segura con facilidad. Mejoren la eficiencia de su equipo con agentes que simplifican los flujos de trabajo académicos y operativos con sólidas funciones de seguridad y confianza integradas. Esto proporciona herramientas y recursos clave para ayudarlos a:
Crear agentes específicos de dominio para automatizar tareas complejas.
Utilizar la identidad de nivel empresarial para los agentes y la IA confiable integrada.
Implementar y escalar agentes de manera rápida, con infraestructura administrada.
Con Azure AI Foundry Agent Service, el equipo puede crear agentes específicos de dominio adaptados a sus necesidades únicas. Les ayuda a diseñar, implementar y escalar agentes que están listos para su uso en el mundo real. Este servicio administrado por completo, maneja la infraestructura y la orquestación. Incluye plantillas, acciones y conectores listos para usar para más de 1.400 orígenes de datos empresariales, incluidos SharePoint, Microsoft Fabric y sistemas de terceros. Por ejemplo, puede diseñar e implementar agentes para ayudar a incorporar nuevos estudiantes con orientación personalizada y apoyar a los equipos administrativos con respuestas instantáneas a preguntas comunes.
Instituciones como Stanford Medicine ya han comenzado a usar el orquestador de agentes de atención médica en Azure AI Foundry junto con Microsoft Copilot Studio. Esta integración mejora la eficiencia de las reuniones de la junta de tumores a través de flujos de trabajo clínicos personalizados.
Protejan y administren a sus agentes
La creación de agentes es solo el comienzo: administrarlos de manera responsable juega un papel fundamental en su uso efectivo. Con el identificador de agente de Microsoft Entra, ustedes pueden:
Obtener visibilidad y control completos sobre las acciones de los agentes.
Asignar identidades únicas para cada agente.
Compartir la administración de identidades con los miembros de su equipo.
Definir controles de acceso y permisos para cada agente.
La IA confiable es un compromiso fundamental para Microsoft y para nuestros clientes. Hemos introducido nuevas capacidades para ayudar a las instituciones a descubrir, proteger y gobernar los sistemas de IA desde el principio.
En el lado de la seguridad, Azure AI Foundry se integra con Microsoft Defender for Cloud para proporcionar alertas e información en tiempo real cuando surgen amenazas. Para el cumplimiento, la integración lista para usar con herramientas de gobernanza como Credo AI, Saidot y Microsoft Purview, ayuda a las instituciones a monitorear el rendimiento del modelo, evaluar la equidad y realizar un seguimiento de los requisitos normativos.
Mediante el uso de las herramientas integradas de Azure AI Foundry para la seguridad, la protección y la gobernanza, las instituciones pueden diseñar e implementar sistemas de IA con mayor confiabilidad desde el principio.
Impulsar la próxima frontera de la IA con Microsoft Fabric
Los datos sólidos son fundamentales para una IA eficaz. Microsoft Fabric ayuda a unificar los datos para potenciar el análisis y los agentes, sin la carga de administrar una infraestructura compleja. Como solución SaaS, Fabric ofrece una integración perfecta de herramientas de datos y puede reducir la necesidad de conexiones de servicio manuales.
En su núcleo se encuentra Microsoft OneLake, un lago de datos abierto y unificado que admite cualquier formato, desde cualquier nube. Esta flexibilidad permite a los desarrolladores acceder y analizar todo tipo de datos de manera eficiente.
Fabric también transforma la forma en que ustedes administran e interactúan con los datos. Con las capacidades de lenguaje natural, pueden explorar información que impulse el éxito de los estudiantes, mejore la investigación y aumente la agilidad operativa, lo que permite a todos tomar decisiones informadas y basadas en datos.
Encuentren los datos correctos cuando los necesiten
Los líderes educativos necesitan herramientas que conviertan los conocimientos en acción. Es por eso que nos enfocamos en hacer que los datos sean más accesibles a través de experiencias conversacionales. Con Copilot en Power BI, los usuarios ahora pueden hacer preguntas en lenguaje natural y recibir información instantánea, sin necesidad de formación técnica para empezar. Ya sea que se trate de tendencias de inscripción, riesgos de retención o donaciones de ex alumnos, los profesores y el personal pueden explorar datos de manera directa dentro de Microsoft Teams, para optimizar su flujo de trabajo.
Capaciten a todos para que interactúen con sus datos
Con las capacidades de interacción mejoradas de Power BI y Copilot Studio, la transformación de los datos en información procesable ahora puede ser más rápida e intuitiva. Ustedes pueden explorar datos a través de experiencias conversacionales naturales, para eliminar la complejidad y hacer que el análisis sea más accesible. Este cambio les permite a ustedes y a sus equipos romper los silos de datos y descubrir información valiosa con facilidad. El chat de Power BI simplifica la exploración de conjuntos de datos complejos, para ofrecer una toma de decisiones más rápida y segura.
Descubran información más detallada con agentes de datos
Por último, conectar los agentes de datos de Fabric a Copilot Studio puede ayudar a descubrir información más profunda. Estos agentes analizan de manera experta conjuntos de datos complejos, para descubrir información valiosa de OneLake e impulsar acciones informadas. Al automatizar tareas como el envío de correos electrónicos y la activación del flujo de trabajo, agilizan sus interacciones con los datos empresariales, lo que permite una toma de decisiones segura.
Adopten el futuro de la educación superior basada en datos con Microsoft Azure y Azure AI Foundry. Descubran cómo los agentes de datos innovadores y los conocimientos impulsados por IA pueden mejorar su enfoque del aprendizaje y las operaciones. Comiencen su recorrido hoy y descubran las posibilidades ilimitadas que les esperan.
Amazon QuickSight has increased the limits on number of calculated fields allowed in an analysis from 500 to 2000, and from 200 to 500 per dataset. This update enables authors and data curators to create more transformations on their data and draw additional complex insights. This is especially useful for authors and data curators who work with really large datasets and cater to multiple end user personas.
Amazon QuickSight has increased the limits on number of calculated fields allowed in an analysis from 500 to 2000, and from 200 to 500 per dataset. This update enables authors and data curators to create more transformations on their data and draw additional complex insights. This is especially useful for authors and data curators who work with really large datasets and cater to multiple end user personas. In QuickSight, users can also use natural language to build calculations using Q. The new calculated fields limits are now available in all supported Amazon QuickSight regions. To learn more about calculated fields and other QuickSight limits, visit item limits for analysis.
