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Amazon RDS for SQL Server now support BYOM in additional commercial regions

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Bring Your Own Media (BYOM) in 10 additional AWS Regions: Asia Pacific (Taipei, Hyderabad, Jakarta, Malaysia, Melbourne, New Zealand, Thailand), Europe (Milan, Spain), and Mexico (Central).

With BYOM, you can adopt Amazon RDS as a managed database service and reuse your existing Microsoft SQL Server licenses with active Software Assurance, through Microsoft’s License Mobility program. BYOM is supported on SQL Server 2019, 2022, and 2025. Amazon RDS for SQL Server BYOM is integrated with AWS License Manager so you can track license usage across your AWS environment for compliance.

To learn more, see Bring Your Own Media (BYOM) for RDS for SQL Server. For pricing and regional availability, see Amazon RDS for SQL Server Pricing.

 

​Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Bring Your Own Media (BYOM) in 10 additional AWS Regions: Asia Pacific (Taipei, Hyderabad, Jakarta, Malaysia, Melbourne, New Zealand, Thailand), Europe (Milan, Spain), and Mexico (Central). With BYOM, you can adopt Amazon RDS as a managed database service and reuse your existing Microsoft SQL Server licenses with active Software Assurance, through Microsoft’s License Mobility program. BYOM is supported on SQL Server 2019, 2022, and 2025. Amazon RDS for SQL Server BYOM is integrated with AWS License Manager so you can track license usage across your AWS environment for compliance. To learn more, see Bring Your Own Media (BYOM) for RDS for SQL Server. For pricing and regional availability, see Amazon RDS for SQL Server Pricing.  

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Dentro de organizaciones sin fines de lucro que utilizan IA para ampliar su impacto

Dentro de organizaciones sin fines de lucro que utilizan IA para ampliar su impacto

Transformen la oportunidad en impacto con la IA

Por: Jill Tennant, líder global de marketing de Microsoft Elevate.

Un refugio de animales pronostica dónde se necesitarán hogares de acogida antes de que la «temporada de gatitos» alcance su punto álgido. Investigadores que estudian una enfermedad neurodegenerativa que afecta a cientos de miles de personas aceleran el progreso hacia tratamientos y curas. Una pequeña organización sin fines de lucro dedica más tiempo a mentorizar a jóvenes y menos tiempo enterrada en papeleo.

Estas historias no tienen mucho en común en la superficie, pero juntas apuntan a un cambio más amplio: la IA ayuda a organizaciones sin fines de lucro de todo el mundo a afrontar algunos de los desafíos más urgentes de la sociedad.

Animal Protection Denmark, Answer ALS y Everything Suarve son tres organizaciones sin fines de lucro que recurrieron a Microsoft para resolver diferentes problemas. Hoy en día, la IA les ayuda a ampliar oportunidades, fortalecer comunidades y crear nuevas posibilidades para las personas, animales y causas a las que sirven—para ampliar su impacto mucho más allá de lo que su tamaño o recursos podrían permitir.

Descubran cómo Microsoft ayuda a las organizaciones sin fines de lucro a acelerar sus misiones

Animal Protection Denmark: De la conjetura a la visión en el cuidado de gatitos

Cada año, Animal Protection Denmark se prepara para la «temporada de gatitos», cuando los refugios ven un aumento de animales vulnerables que necesitan cuidados. A medida que la organización crecía, también lo hacía el volumen de datos que circulaban entre refugios, redes de acogida, voluntarios y simpatizantes. Los equipos a menudo dedicaban largas horas cada mes a reconciliar información de diferentes sistemas, lo que dificultaba prever necesidades y coordinar recursos.

A través de la utilización de datos de Microsoft y capacidades de IA, Animal Protection Denmark unificó esos datos en una única fuente de verdad. Los equipos pueden identificar tendencias antes, prever la demanda de colocaciones en acogida y monitorizar la capacidad de los refugios en tiempo real. El personal también puede acceder a registros completos de cada animal, lo que ayuda a garantizar la continuidad del cuidado y apoyar decisiones de adopción más rápidas.

Al combinar una base de datos unificada con conocimientos impulsados por IA, Animal Protection Denmark dedica menos tiempo a gestionar la información y más tiempo a actuar sobre ella. El resultado es una planificación más temprana, decisiones más rápidas y mejores resultados para los animales que dependen de ellas.

Lean cómo Animal Protection Denmark amplía su impacto con Microsoft

Answer ALS: Convertir datos en descubrimiento

Answer ALS se creó para acelerar el avance hacia tratamientos y una cura para la esclerosis lateral amiotrófica (ELA, por sus siglas en inglés), una enfermedad neurodegenerativa progresiva que afecta a más de 450.000 personas en todo el mundo. Junto con Microsoft, la organización construyó Neuromine, uno de los mayores centros de investigación sobre ELA del mundo, para reunir billones de puntos de datos aportados por más de 2.500 personas que viven con la enfermedad. Azure AI Search impulsa la función de consulta de Neuromine para que los investigadores puedan obtener con rapidez, detalles sobre la trayectoria de la enfermedad de una persona o incluso su ADN, lo que ayuda a localizar líneas celulares para estudios posteriores.

Además, Answer ALS está en proceso de desarrollo de un chatbot en Microsoft Foundry que utiliza IA generativa para responder preguntas de los usuarios y dirigir a los investigadores hacia datos relevantes. Antes de Neuromine, los investigadores solían pasar meses, y a veces más de un año, en reunir datos y muestras biológicas antes de que pudiera comenzar un análisis significativo. Hoy en día, investigadores de todo el mundo pueden acceder a cientos de líneas de pacientes y datos clínicos relacionados en horas en lugar de meses, ayudándoles a pasar más rápido de las preguntas a las perspectivas.

Al facilitar la exploración, el intercambio y el desarrollo de datos de investigación de alta calidad, Answer ALS ayuda a acelerar la investigación hasta en un 65% en los próximos años. La plataforma permite a investigadores de todo el mundo colaborar de forma más eficaz, descubrir nuevos patrones en los datos y avanzar en la búsqueda de mejores tratamientos y, en última instancia, de una cura.

Descubran cómo Answer ALS avanza hacia tratamientos y curas con Microsoft Azure

Everything Suarve: Escalar segundas oportunidades con IA

Everything Suarve ayuda a los jóvenes en Australia a reconstruir sus vidas mediante formación laboral, mentoría, apoyo en salud mental y habilidades prácticas para la vida. Muchos participantes llegan tras enfrentarse a desafíos como la inseguridad en la vivienda, entornos familiares inestables o traumas. Para un equipo pequeño, el trabajo depende de la confianza, la constancia y de estar presente para los jóvenes cuando más necesitan apoyo.

A medida que crecía la demanda de sus programas, también lo hacía la carga administrativa. Las derivaciones llegaban por correo electrónico, formularios en papel y llamadas telefónicas, mientras el personal seguía el progreso de los participantes a través de sistemas desconectados. Para agilizar las operaciones, Everything Suarve utilizó datos de Microsoft y capacidades de IA para construir una solución que centraliza la inscripción, la gestión de casos, la presentación de informes y las comunicaciones. La organización también utiliza Microsoft 365 Copilot para ayudar con la redacción de subvenciones, el resumen de documentos y otras tareas administrativas, reduciendo el tiempo dedicado al trabajo manual.

Hoy en día, el equipo de Everything Suarve puede gestionar derivaciones, notas de casos, informes y comunicaciones con los participantes en un único flujo de trabajo. La nueva plataforma ahorra hasta ocho horas por participante durante la inscripción, mientras que Copilot reduce el trabajo de solicitud de subvenciones hasta en dos semanas. Al automatizar procesos y poner la IA en marcha, Everything Suarve reduce la carga administrativa y crea más tiempo para ayudar a los jóvenes a ganar confianza, habilidades y un camino hacia el empleo, la educación y la estabilidad a largo plazo.

Descubran cómo Everything Suarve ayuda a los jóvenes a prosperar con Microsoft

¿Qué pueden aprender otras organizaciones sin fines de lucro de estas historias

Estas tres organizaciones sirven a diferentes comunidades y persiguen objetivos distintos. Sin embargo, juntas demuestran que las organizaciones generan el mayor valor de la IA cuando la utilizan para fortalecer la experiencia humana y partir de un problema que merece la pena resolver o una misión que merece ser impulsada.

En Animal Protection Denmark, el personal puede detectar necesidades antes de que alcancen su pico. En Answer ALS, los investigadores comprimen años de descubrimientos en meses. En Everything Suarve, el personal dedica menos tiempo a los formularios y más tiempo con los jóvenes que reconstruyen sus vidas.

En conjunto, estas historias reflejan un cambio más amplio que ya está en marcha en organizaciones de todos los tamaños. La IA se ha comenzado a convertir en parte de cómo se toman las decisiones, cómo se prestan los servicios y cómo trabajan las personas. El éxito ya no se define solo por la tecnología. Proviene de combinar la tecnología con el juicio, la experiencia y el propósito humanos.

A esto lo llamamos Frontier Transformation: ir más allá de herramientas aisladas y tareas individuales para replantearse cómo cumplen su misión. A veces el resultado es una mayor eficiencia. A menudo, crea algo más significativo: la capacidad de llegar a más personas, responder más rápido y extender el impacto de formas que antes estaban fuera de su alcance.

A medida que la IA se vuelve más accesible, estas organizaciones sin fines de lucro demuestran que la transformación significativa no está reservada para las grandes empresas. Puede comenzar en cualquier lugar donde la gente esté dispuesta a combinar la innovación con un claro sentido de propósito.

Descubran cómo Microsoft Elevate para ONGs puede ayudar a ampliar el impacto social

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OpenAI GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock

GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.

With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a 90% discount. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments.

GPT-5.6 Sol is available in the following AWS Regions: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.

 

​GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.
With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a 90% discount. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments.
GPT-5.6 Sol is available in the following AWS Regions: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.  

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AWS Transform continuous modernization is now generally available

AWS Transform continuous modernization is now generally available in all AWS Regions where AWS Transform is supported. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria.

With today’s launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control.

You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the AWS Transform web application or use the AWS Transform Kiro Power and agent plugins. To learn more, see AWS Transform continuous modernization in the AWS Transform User Guide.

 

​AWS Transform continuous modernization is now generally available in all AWS Regions where AWS Transform is supported. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria. With today’s launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control. You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the AWS Transform web application or use the AWS Transform Kiro Power and agent plugins. To learn more, see AWS Transform continuous modernization in the AWS Transform User Guide.  

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AWS Organizations now provides maximum account quota visibility in Service Quotas

AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit.

Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas GetServiceQuota API.

This quota visibility is available now available in US East (N. Virginia). To learn more, see viewing service quotas in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the AWS Organizations documentation.

 

​AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit. Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas GetServiceQuota API. This quota visibility is available now available in US East (N. Virginia). To learn more, see viewing service quotas in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the AWS Organizations documentation.  

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Amazon GameLift Streams now supports sharing streams with stream URLs

Amazon GameLift Streams now offers stream URLs, which give end users temporary, unauthenticated access to a playable stream session in a supported web browser. Recipients need no AWS account, no credentials, and no software install.

To share a playable stream, create a stream URL for a stream group and one of its applications, set how long the stream URL stays valid and how many sessions it can start, and send the link. Each person who opens the link starts an independent stream session, and Amazon GameLift Streams routes them to a nearby streaming location from the locations you selected. No client integration or backend service is required. You can create, monitor, and revoke stream URLs in the Amazon GameLift Streams console or with the new CreateStreamUrl, GetStreamUrl, ListStreamUrls, and RevokeStreamUrl APIs.

There is no additional charge for stream URLs. You are charged for the stream capacity that sessions started from a stream URL consume, as described on the Amazon GameLift Streams pricing page. For a full list of supported Regions, see the AWS Region table.

To get started, see Share stream sessions with stream URLs in the Amazon GameLift Streams Developer Guide and the CreateStreamUrl API Reference. To learn more about the service, see the Amazon GameLift Streams product page. 

 

​Amazon GameLift Streams now offers stream URLs, which give end users temporary, unauthenticated access to a playable stream session in a supported web browser. Recipients need no AWS account, no credentials, and no software install.
To share a playable stream, create a stream URL for a stream group and one of its applications, set how long the stream URL stays valid and how many sessions it can start, and send the link. Each person who opens the link starts an independent stream session, and Amazon GameLift Streams routes them to a nearby streaming location from the locations you selected. No client integration or backend service is required. You can create, monitor, and revoke stream URLs in the Amazon GameLift Streams console or with the new CreateStreamUrl, GetStreamUrl, ListStreamUrls, and RevokeStreamUrl APIs.
There is no additional charge for stream URLs. You are charged for the stream capacity that sessions started from a stream URL consume, as described on the Amazon GameLift Streams pricing page. For a full list of supported Regions, see the AWS Region table.
To get started, see Share stream sessions with stream URLs in the Amazon GameLift Streams Developer Guide and the CreateStreamUrl API Reference. To learn more about the service, see the Amazon GameLift Streams product page.   

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Amazon EC2 I7i instances now available in Asia Pacific (Thailand) and Israel (Tel Aviv) Regions

Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances.

I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks.

I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page.

 

​Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks. I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page.  

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AWS HealthOmics now supports task-level timeout for WDL workflows

AWS HealthOmics now supports task-level timeout for Workflow Description Language (WDL) workflows, enabling you to set maximum execution duration for individual tasks. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. 

With task-level timeout, you can define time bounds on individual WDL tasks to control costs and enable automated error recovery. HealthOmics provides the omicsTimeout runtime attribute that you can add to any task’s runtime section to specify the maximum duration a task is allowed to run. When a task exceeds the specified duration, HealthOmics stops the task and sets the task and run statuses to failed. The omicsTimeout attribute accepts duration values with standard time units (such as 90s, 2h, 1d). This prevents tasks from consuming resources and helps you set cost guardrails during workflow development. 

Task-level timeout for WDL workflows is available in all supported AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the WDL workflow definition specifics documentation. 

 

​AWS HealthOmics now supports task-level timeout for Workflow Description Language (WDL) workflows, enabling you to set maximum execution duration for individual tasks. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. 
With task-level timeout, you can define time bounds on individual WDL tasks to control costs and enable automated error recovery. HealthOmics provides the omicsTimeout runtime attribute that you can add to any task’s runtime section to specify the maximum duration a task is allowed to run. When a task exceeds the specified duration, HealthOmics stops the task and sets the task and run statuses to failed. The omicsTimeout attribute accepts duration values with standard time units (such as 90s, 2h, 1d). This prevents tasks from consuming resources and helps you set cost guardrails during workflow development. 
Task-level timeout for WDL workflows is available in all supported AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the WDL workflow definition specifics documentation.   

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AWS Resilience Hub now provides recommended resilience tests

AWS Resilience Hub now offers recommended resilience tests that help platform engineering and site reliability teams validate how their services respond to and recover from known failure scenarios. 

Resilience Hub provides pre-configured tests based on your service’s architecture, configuration, and resilience policy. It uses AWS Fault Injection Service (FIS) to inject controlled faults and then evaluates whether your service recovers within your defined recovery objectives. With the AWS-recommended resilience tests, teams can validate readiness for scenarios such as Availability Zone impairment, Regional impairment, and dependency failure. Each test automatically targets resources in the service, injects the required faults, produces a pass or fail outcome based on alarm evaluation and recovery objectives, then generates a detailed test report.

The recommended testing on the next generation of the AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo).

To get started, visit the AWS console. To learn more about recommended resilience testing see the product page for the next generation of AWS Resilience Hub. 

 

​AWS Resilience Hub now offers recommended resilience tests that help platform engineering and site reliability teams validate how their services respond to and recover from known failure scenarios. 
Resilience Hub provides pre-configured tests based on your service’s architecture, configuration, and resilience policy. It uses AWS Fault Injection Service (FIS) to inject controlled faults and then evaluates whether your service recovers within your defined recovery objectives. With the AWS-recommended resilience tests, teams can validate readiness for scenarios such as Availability Zone impairment, Regional impairment, and dependency failure. Each test automatically targets resources in the service, injects the required faults, produces a pass or fail outcome based on alarm evaluation and recovery objectives, then generates a detailed test report.
The recommended testing on the next generation of the AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo).
To get started, visit the AWS console. To learn more about recommended resilience testing see the product page for the next generation of AWS Resilience Hub.   

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Amazon SageMaker AI serverless model customization now supports full fine-tuning

Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it.

Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use.

Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK. To learn more, and see the supported list of models, see the Amazon SageMaker AI model customization documentation.

 

​Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it. Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use. Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK. To learn more, and see the supported list of models, see the Amazon SageMaker AI model customization documentation.