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Amazon SageMaker Studio now supports remote connections from Visual Studio Code

Today, AWS announces remote connection from Visual Studio Code to Amazon SageMaker Studio development environments, enabling AI developers to use Visual Studio Code with SageMaker AI’s scalable compute resources. This new capability enables developers to connect from Visual Studio Code to SageMaker Studio in minutes instead of hours, enabling them to rapidly scale their model development.

SageMaker Studio offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (VS Code – Open Source). Starting today, you can use your customized local VS Code setup, including AI-assisted development tools and custom extensions, while accessing SageMaker AI’s compute resources and your data. You can authenticate using the AWS Toolkit extension in VS Code or through SageMaker Studio’s web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks. You maintain the same security boundaries as SageMaker Studio’s web-based environments while developing AI models and analyzing data in Visual Studio Code.

This feature is now available in US East (Ohio) Region.

To learn more, see the following resources:

 

​Today, AWS announces remote connection from Visual Studio Code to Amazon SageMaker Studio development environments, enabling AI developers to use Visual Studio Code with SageMaker AI’s scalable compute resources. This new capability enables developers to connect from Visual Studio Code to SageMaker Studio in minutes instead of hours, enabling them to rapidly scale their model development.
SageMaker Studio offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (VS Code – Open Source). Starting today, you can use your customized local VS Code setup, including AI-assisted development tools and custom extensions, while accessing SageMaker AI’s compute resources and your data. You can authenticate using the AWS Toolkit extension in VS Code or through SageMaker Studio’s web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks. You maintain the same security boundaries as SageMaker Studio’s web-based environments while developing AI models and analyzing data in Visual Studio Code.
This feature is now available in US East (Ohio) Region.
To learn more, see the following resources:

Amazon SageMaker Studio webpage
AWS ML Blog
Documentation  

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Amazon SageMaker HyperPod announces new observability capability

Amazon SageMaker HyperPod’s new observability capability allows customers to accelerate generative AI model development by providing comprehensive visibility across compute resources and model development tasks. It takes away the manual work of collecting hundreds of metrics from across the stack, visualizing the correlations between them, and restoring the generative AI model development task performance. HyperPod observability tracks task performance metrics in real-time, alerts customers when any of them deteriorate, and automatically remediates the root cause with customer-defined policies.

SageMaker HyperPod observability transforms how customers monitor and optimize their generative AI model development tasks. Through a unified dashboard pre-configured in Amazon Managed Grafana with the monitoring data automatically published to an Amazon Managed Prometheus workspace, customers can now see generative AI task performance metrics, resource utilization, and cluster health in a single view. This allows teams to quickly spot bottlenecks, prevent costly delays, and optimize compute resources. Customers can define automated alerts, derive use-case specific task metrics, and publish them to the unified dashboard with just a few clicks. By reducing troubleshooting time from days to minutes, this capability helps customers accelerate their path to production and maximize the return on their AI investments.

SageMaker HyperPod observability is available in all AWS Regions where SageMaker HyperPod is supported, except US West (N. California) and Asia Pacific (Melbourne). To learn more and get started, visit the blog, documentation, and SageMaker HyperPod webpage.

 

​Amazon SageMaker HyperPod’s new observability capability allows customers to accelerate generative AI model development by providing comprehensive visibility across compute resources and model development tasks. It takes away the manual work of collecting hundreds of metrics from across the stack, visualizing the correlations between them, and restoring the generative AI model development task performance. HyperPod observability tracks task performance metrics in real-time, alerts customers when any of them deteriorate, and automatically remediates the root cause with customer-defined policies.
SageMaker HyperPod observability transforms how customers monitor and optimize their generative AI model development tasks. Through a unified dashboard pre-configured in Amazon Managed Grafana with the monitoring data automatically published to an Amazon Managed Prometheus workspace, customers can now see generative AI task performance metrics, resource utilization, and cluster health in a single view. This allows teams to quickly spot bottlenecks, prevent costly delays, and optimize compute resources. Customers can define automated alerts, derive use-case specific task metrics, and publish them to the unified dashboard with just a few clicks. By reducing troubleshooting time from days to minutes, this capability helps customers accelerate their path to production and maximize the return on their AI investments.
SageMaker HyperPod observability is available in all AWS Regions where SageMaker HyperPod is supported, except US West (N. California) and Asia Pacific (Melbourne). To learn more and get started, visit the blog, documentation, and SageMaker HyperPod webpage.  

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Anthropic’s Claude 3.7 Sonnet is now available on Amazon Bedrock in AWS GovCloud (US-West)

Anthropic’s Claude 3.7 Sonnet hybrid reasoning model is now available in AWS GovCloud (US-West). Claude 3.7 Sonnet offers advanced AI capabilities with both quick responses and extended, step-by-step thinking made visible to the user. This model has strong capabilities in coding and brings enhanced performance across various tasks, like instruction following, math, and physics. Anthropic’s Claude 3.7 Sonnet model is also FedRAMP High and Department of Defense Cloud Computing Security Requirements Guide (DoD CC SRG) Impact Level (IL) 4 and 5 approved within Amazon Bedrock in the AWS GovCloud (US) Regions.

Claude 3.7 Sonnet introduces a unique approach to AI reasoning by integrating it seamlessly with other capabilities. Unlike traditional models that separate quick responses from those requiring deeper thought, Claude 3.7 Sonnet allows users to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. In extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks. Amazon Bedrock customers can adjust how long the model thinks, offering a flexible trade-off between speed and answer quality. Additionally, users can control the reasoning budget by specifying a token limit, enabling more precise cost management.

Claude 3.7 Sonnet is also available on Amazon Bedrock in the Europe (London), Europe (Frankfurt), Europe (Ireland), Europe (Paris), Europe (Stockholm), US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information, see the AWS News Blog, Claude in Amazon Bedrock and the Amazon Bedrock documentation.

 

​Anthropic’s Claude 3.7 Sonnet hybrid reasoning model is now available in AWS GovCloud (US-West). Claude 3.7 Sonnet offers advanced AI capabilities with both quick responses and extended, step-by-step thinking made visible to the user. This model has strong capabilities in coding and brings enhanced performance across various tasks, like instruction following, math, and physics. Anthropic’s Claude 3.7 Sonnet model is also FedRAMP High and Department of Defense Cloud Computing Security Requirements Guide (DoD CC SRG) Impact Level (IL) 4 and 5 approved within Amazon Bedrock in the AWS GovCloud (US) Regions. Claude 3.7 Sonnet introduces a unique approach to AI reasoning by integrating it seamlessly with other capabilities. Unlike traditional models that separate quick responses from those requiring deeper thought, Claude 3.7 Sonnet allows users to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. In extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks. Amazon Bedrock customers can adjust how long the model thinks, offering a flexible trade-off between speed and answer quality. Additionally, users can control the reasoning budget by specifying a token limit, enabling more precise cost management. Claude 3.7 Sonnet is also available on Amazon Bedrock in the Europe (London), Europe (Frankfurt), Europe (Ireland), Europe (Paris), Europe (Stockholm), US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information, see the AWS News Blog, Claude in Amazon Bedrock and the Amazon Bedrock documentation.  

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3 formas en que la IA impulsa la evolución de los servicios sociales en el gobierno

julio 10, 2025

3 formas en que la IA impulsa la evolución de los servicios sociales en el gobierno

Colegas intercambian ideas en un espacio de oficina moderna

Por: Eric Basha, director de estrategia de negocios, industria gubernamental mundial.

Un anciano indígena que padece problemas de salud crónicos en una aldea remota necesita ayuda para viajar y recibir atención médica. Una madre soltera en una ciudad abarrotada pierde su trabajo y busca beneficios de desempleo y cuidado de niños. Un joven trabajador en un país multilingüe no puede acceder a la ayuda para la vivienda porque no habla el idioma oficial.

Estos son solo algunos de la muy amplia gama de escenarios en los que las personas de todo el mundo acuden a las entidades gubernamentales de servicios sociales en busca de ayuda y apoyo. De hecho, más de la mitad de la población mundial (52,4%) está cubierta por al menos una prestación de protección social.1 A medida que estos servicios se expanden, las organizaciones y agencias públicas dedicadas se esfuerzan por administrar programas de beneficios, mejorar el acceso a la atención médica y proteger a las poblaciones vulnerables, incluso cuando enfrentan una presión cada vez mayor para hacer más con menos.

Ayudar a las agencias y organizaciones gubernamentales a explorar el potencial de la IA y crear nuevas soluciones que generen un impacto a corto plazo y una transformación a largo plazo es fundamental para nuestro trabajo en Microsoft for Government. Cultivamos asociaciones duraderas con organizaciones gubernamentales de todo tipo para ayudar a innovar y brindar servicios seguros y confiables que promuevan la seguridad, la salud y la prosperidad. 

Descubrir soluciones con Microsoft para la Administración Pública

Cómo la IA generativa abre nuevas vías de impacto

Impulsada por la convergencia de los desafíos modernos, la IA se ha convertido con rapidez en una solución transformadora única en la prestación de servicios sociales. Las presiones presupuestarias y de mano de obra, la proliferación de datos y la demanda de los electores de servicios que reflejen las ofertas del sector privado se suman a la presión. Y eso sin mencionar el aumento de las amenazas cibernéticas y la complejidad de los negocios y la tecnología.

La IA generativa, con sus capacidades únicas para sintetizar datos, comprender el lenguaje natural, retener información contextual, resumir contenido y escribir documentos y código, es en especial adecuada para ayudar a responder a estos desafíos. Con soluciones potentes como Microsoft 365 Copilot, agentes y chatbots desarrollados a medida, y otras innovaciones que integran la IA en los flujos de trabajo y procesos habituales, los gobiernos tienen la oportunidad no solo de arreglar lo viejo, sino de inventar lo nuevo.  

En todo el mundo, las agencias y organizaciones han tenido un éxito notable en los primeros casos de uso de IA diseñados para ayudar a mejorar la eficiencia, optimizar la prestación de servicios y obtener información valiosa a partir de datos y análisis predictivos. Estos son tres ejemplos de impacto crítico que hemos visto en el último año:

1. Mejora de las experiencias de los constituyentes con un acceso más fácil a la información

A medida que crecen las expectativas de servicios digitales rápidos y personalizados, muchos gobiernos han comenzado a ver un impacto inmediato con chatbots impulsados por IA u otros asistentes virtuales para manejar una variedad de consultas y asistencia.

Estas innovaciones están disponibles a cualquier hora del día y están bien equipadas para manejar grandes volúmenes de solicitudes de ayuda con cosas como licencias, tránsito, impuestos y más. Permiten que las personas interactúen en el canal de su elección, como llamadas telefónicas, chat digital y redes sociales, y utilicen diferentes idiomas para obtener con rapidez la información correcta, solicitar beneficios, recibir actualizaciones e informar incidentes.

Un gran ejemplo es un chatbot llamado Boti, que el gobierno de la Ciudad de Buenos Aires renovó de manera reciente a través de los servicios de Microsoft Azure OpenAI para revolucionar las interacciones públicas. Entrenado en una extensa base de datos gubernamental, el chatbot utiliza la interacción del lenguaje natural para manejar 2 millones de consultas por mes, para ayudar a los ciudadanos a encontrar servicios, desde servicios básicos como renovaciones de licencias de conducir hasta información de salud pública e información personalizada para turistas. En el camino, ha reducido la carga operativa en un 50%. 

La belleza de este tipo de soluciones es que alivian la carga de encontrar y obtener el mejor servicio posible, incluso cuando las personas tienen poca idea de a quién o qué agencia contactar. La IA hace que sea más fácil para un electorado explorar sus opciones. Y luego, cuando se comprometen, solo necesitan proporcionar su información crítica una vez.

No obligar a alguien a proporcionar de manera continua la misma información a medida que se mueve por el sistema es una gran consideración en los casos en que las personas han experimentado eventos traumáticos, emocionales o vergonzosos. La participación se tensa cuando una persona se ve obligada a volver a explicar y revivir experiencias desagradables. Por lo tanto, la capacidad de la IA para retener detalles esenciales a través de un proceso de gestión de casos y retener el contexto de las consultas ayuda a garantizar una experiencia que no solo sea más eficiente, sino también más digna.

La IA también desempeña un papel importante a la hora de ayudar a los electores cuando no están satisfechos con sus servicios. Un centro de contacto impulsado por IA, como Microsoft Dynamics 365 Contact Center, puede proporcionar nuevos niveles de soporte que pueden mejorar la toma de decisiones humana. Por ejemplo, un centro de contacto impulsado por IA puede desencadenar una derivación a un representante de servicio al cliente cuando el análisis de sentimientos detecta que una persona se siente frustrada o molesta. Mediante el uso de enrutamiento inteligente, puede conectar al constituyente con el mejor representante en función del contexto y la necesidad, y ayudar al representante a resumir la situación de la persona, y sugerir soluciones óptimas e incluso redactar recomendaciones de respuesta. 

2. Aumento de la eficiencia y la eficacia del personal

Uno de los avances más vitales en la evolución digital del gobierno es el paso de las tareas engorrosas que involucran sitios web anticuados, formularios electrónicos, incluso procesos basados en papel, a sistemas automatizados e inteligentes que no solo facilitan la recopilación de datos, sino que también interpretan los datos, aprenden de ellos e incluso actúan en consecuencia.

Con la IA que actúa como un asistente inteligente y siempre presente, los trabajadores sociales y los cuidadores pueden centrarse más en ayudar a las personas y dedicar menos tiempo a tareas tediosas que antes. Estas nuevas herramientas brindan a los trabajadores acceso instantáneo a información relevante de todos los silos de datos, incluidos datos no estructurados como contenido en archivos PDF, archivos, sitios web e incluso documentos escritos a mano digitalizados, todos los cuales no habían estado disponibles para el análisis antes.

Por ejemplo, el Consejo Municipal del Condado de Torfaen en Gales, Reino Unido, vio aumentos en la productividad después de adoptar Microsoft 365 Copilot, que integra la IA generativa en aplicaciones cotidianas como Word, Excel y Outlook. El proceso de tomar y registrar notas, por ejemplo, se ha simplificado de manera importante, lo que libera a los trabajadores para que pasen más tiempo en interactuar con los residentes y brindar servicios personalizados. 

Con la ayuda de la asistencia de la IA, un trabajador social puede atender a los electores de forma mucho más eficaz. Las reuniones con los clientes, por ejemplo, pueden transformarse por completo. La preparación de reuniones se puede realizar de forma más rápida y mucho más completa, con información y recomendaciones extraídas de la información de toda la empresa, incluidos los archivos que antes eran inaccesibles, restringidos o difíciles de extraer información significativa. La reunión puede grabarse y transcribirse de manera automática, lo que permite al trabajador social centrarse en su cliente en lugar de tomar notas. Después, Microsoft Teams puede transcribir y resumir la reunión, con detalles y elementos de acción importados directo a los sistemas de gestión de casos.

3. Mejora de los procesos y los resultados con análisis avanzados

Quizás el aspecto más transformador de la IA es el poder de la analítica avanzada. Esto se refiere a la capacidad única de la IA para convertir los datos sin procesar en información procesable mediante la identificación de patrones, la creación de conexiones e incluso la predicción de resultados. En los servicios sociales y de salud, esto puede traducirse en una variedad de beneficios útiles.

Por ejemplo, la IA puede ayudar a convertir el proceso, a menudo engorroso, de evaluar las solicitudes de beneficios u otros servicios sociales en un proceso más rápido, preciso y fácil de usar. Puede analizar la información con respecto a las reglas de la política, interpretar las regulaciones para ayudar a garantizar que se cumplan los criterios y verificar los datos presentados con los registros oficiales. Esto significa menos errores que podrían conducir a aprobaciones o denegaciones incorrectas, y una mayor satisfacción del cliente.

En conjunto, estas habilidades pueden transformar importantes iniciativas de servicios sociales. Por ejemplo, desempeñan un papel crucial en una nueva plataforma digital creada por el Departamento de Servicios Humanos (DHS, por sus siglas en inglés) de Australia Meridional para modernizar la forma en que se gestionan los casos de violencia doméstica de alto riesgo. Antes, las agencias dependían de documentos físicos y hojas de cálculo semiestructuradas de Excel para realizar un seguimiento de los casos, lo que dificultaba el intercambio de información, la toma de decisiones y la coordinación entre las agencias. El nuevo Portal de Seguridad Familiar, que integra la IA con Microsoft Power BI, transformó la respuesta a la violencia doméstica del DHS en un sistema proactivo, con una alta adaptabilidad y basado en evidencia. Las referencias que antes tardaban días ahora se realizan en tiempo real, y 10 agencias ahora comparten datos en un sistema centralizado que es bastante seguro.  

En términos de mejorar la salud pública y el bienestar, la IA y las herramientas analíticas pueden recopilar, analizar e informar sobre datos de salud pública o de programas para obtener una visión holística de las personas que reciben servicios para mejorar la atención. Un trabajador social, por ejemplo, puede usar la IA para ver más allá de los puntos de datos aislados y obtener una visión mucho más completa de la situación, las necesidades y el historial de una persona. Con menos carga administrativa, esto proporciona un contexto crítico para garantizar que el constituyente reciba justo el apoyo adecuado y mejorar la coordinación de la atención y las intervenciones.

El otro beneficio esencial que proporciona la analítica se encuentra en el ámbito del fraude, el despilfarro y el abuso. Al analizar grandes cantidades de información en tiempo real y aprovechar los datos de registros y experiencias anteriores, la IA puede detectar patrones, identificar irregularidades y señalar comportamientos sospechosos de manera mucho más efectiva y rápida que los métodos tradicionales. Esto puede ayudar a las organizaciones a detectar y mitigar los riesgos de fraude de forma proactiva, por ejemplo, al evaluar las presentaciones a medida que llegan en lugar de a través de auditorías, automatizar la verificación en segundos mediante la verificación cruzada de las identificaciones y los detalles de la solicitud, o comparar el comportamiento de un solicitante con las presentaciones anteriores para asegurarse de que sean legítimas.

Avancen en su recorrido hacia la IA

Casi cualquier agencia gubernamental puede obtener beneficios inmediatos de la IA generativa. Sin embargo, para desbloquear todo el poder de la analítica moderna y la IA avanzada, una organización necesita modernizar su entorno en la nube y garantizar un patrimonio de datos listo para la IA.  

El recorrido de cada organización es único y es importante crear una estrategia a largo plazo con socios tecnológicos de confianza. Para ayudar a su organización gubernamental a dar el siguiente paso, pónganse en contacto con su representante local de Microsoft o con un socio tecnológico certificado de Microsoft. Pueden ayudar a explorar opciones, identificar casos de uso y transformar sus ideas en soluciones significativas.

Más información

1 Organización Internacional del Trabajo, «Informe Mundial sobre la Protección Social 2024», septiembre de 2024.

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Amazon P6e-GB200 UltraServers now available for the highest GPU performance in EC2

Today, Amazon announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB200 UltraServers, accelerated by NVIDIA GB200 NVL72. Amazon EC2 P6e-GB200 UltraServers offer the highest GPU-based AI training and inference performance in EC2. They are designed to accelerate training and inference of foundation models (FMs) including next-generation capabilities like reasoning models and agentic AI at the trillion-parameter scale.

With P6e-GB200 UltraServers, customers can access up to 72 Blackwell GPUs within one NVLink domain to leverage 360 petaflops of FP8 compute (without sparsity), 13.4 TB of total high bandwidth memory (HBM3e), and up to 28.8 Tbps of Elastic Fabric Adapter (EFAv4) networking.  P6e-GB200 UltraServers are powered by the AWS Nitro System, allowing customers to reliably and securely scale AI workloads within EC2 UltraClusters to tens of thousands of GPUs. 

P6e-GB200 UltraServers are now available through Amazon EC2 Capacity Blocks for ML in the Dallas Local Zone («us-east-1-dfw-2a»), an extension of the US East (N. Virginia) region. P6e-GB200 UltraServers are available in two sizes: u-p6e-gb200x72 (72 GPUs within NVLink) and u-p6e-gb200x36 (36 GPUs within NVLink).

To learn more, see Amazon EC2 P6e-GB200 UltraServers and P6-B200 instances.

 

​Today, Amazon announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB200 UltraServers, accelerated by NVIDIA GB200 NVL72. Amazon EC2 P6e-GB200 UltraServers offer the highest GPU-based AI training and inference performance in EC2. They are designed to accelerate training and inference of foundation models (FMs) including next-generation capabilities like reasoning models and agentic AI at the trillion-parameter scale.
With P6e-GB200 UltraServers, customers can access up to 72 Blackwell GPUs within one NVLink domain to leverage 360 petaflops of FP8 compute (without sparsity), 13.4 TB of total high bandwidth memory (HBM3e), and up to 28.8 Tbps of Elastic Fabric Adapter (EFAv4) networking.  P6e-GB200 UltraServers are powered by the AWS Nitro System, allowing customers to reliably and securely scale AI workloads within EC2 UltraClusters to tens of thousands of GPUs. 
P6e-GB200 UltraServers are now available through Amazon EC2 Capacity Blocks for ML in the Dallas Local Zone («us-east-1-dfw-2a»), an extension of the US East (N. Virginia) region. P6e-GB200 UltraServers are available in two sizes: u-p6e-gb200x72 (72 GPUs within NVLink) and u-p6e-gb200x36 (36 GPUs within NVLink).
To learn more, see Amazon EC2 P6e-GB200 UltraServers and P6-B200 instances.  

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Fully managed MLflow 3.0 now available on Amazon SageMaker AI

Amazon SageMaker now offers fully-managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your GenAI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.

As customers across industries accelerate their generative AI development, they require capabilities to track experiments, observe behavior, and evaluate performance of models and AI applications. Data scientists and developers lack tools for analyzing the performance of models and AI applications from experimentation to production, making it hard to root cause and resolve issues. Teams spend more time integrating tools than improving their models or generative AI applications. With this launch, fully managed MLflow 3.0 on Amazon SageMaker AI enables customers to accelerate generative AI by making it easier to track experiments and monitor performance of models and AI applications using a single tool. Tracing capabilities in fully managed MLflow 3.0 enable customers to record the inputs, outputs, and metadata at every step of a generative AI application, helping developers quickly identify the source of bugs or unexpected behaviors. By maintaining records of each model and application version, fully managed MLflow 3.0 offers traceability to connect AI responses to their source components, allowing developers to quickly trace an issue directly to the specific code, data, or parameters that generated it. This dramatically reduces troubleshooting time and enables teams to focus more on innovation.

Fully managed MLflow 3.0 on Amazon SageMaker AI is now available in all regions where Amazon SageMaker is offered, excluding China Regions and GovCloud (US) Regions.

To learn more about fully managed MLflow 3.0 on Amazon SageMaker AI, visit the Amazon SageMaker developer guide.

 

​Amazon SageMaker now offers fully-managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your GenAI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.
As customers across industries accelerate their generative AI development, they require capabilities to track experiments, observe behavior, and evaluate performance of models and AI applications. Data scientists and developers lack tools for analyzing the performance of models and AI applications from experimentation to production, making it hard to root cause and resolve issues. Teams spend more time integrating tools than improving their models or generative AI applications. With this launch, fully managed MLflow 3.0 on Amazon SageMaker AI enables customers to accelerate generative AI by making it easier to track experiments and monitor performance of models and AI applications using a single tool. Tracing capabilities in fully managed MLflow 3.0 enable customers to record the inputs, outputs, and metadata at every step of a generative AI application, helping developers quickly identify the source of bugs or unexpected behaviors. By maintaining records of each model and application version, fully managed MLflow 3.0 offers traceability to connect AI responses to their source components, allowing developers to quickly trace an issue directly to the specific code, data, or parameters that generated it. This dramatically reduces troubleshooting time and enables teams to focus more on innovation.
Fully managed MLflow 3.0 on Amazon SageMaker AI is now available in all regions where Amazon SageMaker is offered, excluding China Regions and GovCloud (US) Regions.
To learn more about fully managed MLflow 3.0 on Amazon SageMaker AI, visit the Amazon SageMaker developer guide.  

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Amazon Route 53 Resolver Query Logging now available in Asia Pacific (Taipei)

Today, we are announcing the availability of Route 53 Resolver Query Logging in Asia Pacific (Taipei), enabling you to log DNS queries that originate in your Amazon Virtual Private Cloud (Amazon VPC). With query logging enabled, you can see which domain names have been queried, the AWS resources from which the queries originated – including source IP and instance ID – and the responses that were received.

Route 53 Resolver is the Amazon DNS server that is available by default in all Amazon VPCs. Route 53 Resolver responds to DNS queries from AWS resources within a VPC for public DNS records, Amazon VPC-specific DNS names, and Amazon Route 53 private hosted zones. With Route 53 Resolver Query Logging, customers can log DNS queries and responses for queries originating from within their VPCs, whether those queries are answered locally by Route 53 Resolver, or are resolved over the public internet, or are forwarded to on-premises DNS servers via Resolver Endpoints. You can share your query logging configurations across multiple accounts using AWS Resource Access Manager (RAM). You can also choose to send your query logs to Amazon S3, Amazon CloudWatch Logs, or Amazon Kinesis Data Firehose.

There is no additional charge to use Route 53 Resolver Query Logging, although you may incur usage charges from Amazon S3, Amazon CloudWatch, or Amazon Kinesis Data Firehose. To learn more about Route 53 Resolver Query Logging or to get started, visit the Route 53 product page or the Route 53 documentation

 

​Today, we are announcing the availability of Route 53 Resolver Query Logging in Asia Pacific (Taipei), enabling you to log DNS queries that originate in your Amazon Virtual Private Cloud (Amazon VPC). With query logging enabled, you can see which domain names have been queried, the AWS resources from which the queries originated – including source IP and instance ID – and the responses that were received. Route 53 Resolver is the Amazon DNS server that is available by default in all Amazon VPCs. Route 53 Resolver responds to DNS queries from AWS resources within a VPC for public DNS records, Amazon VPC-specific DNS names, and Amazon Route 53 private hosted zones. With Route 53 Resolver Query Logging, customers can log DNS queries and responses for queries originating from within their VPCs, whether those queries are answered locally by Route 53 Resolver, or are resolved over the public internet, or are forwarded to on-premises DNS servers via Resolver Endpoints. You can share your query logging configurations across multiple accounts using AWS Resource Access Manager (RAM). You can also choose to send your query logs to Amazon S3, Amazon CloudWatch Logs, or Amazon Kinesis Data Firehose. There is no additional charge to use Route 53 Resolver Query Logging, although you may incur usage charges from Amazon S3, Amazon CloudWatch, or Amazon Kinesis Data Firehose. To learn more about Route 53 Resolver Query Logging or to get started, visit the Route 53 product page or the Route 53 documentation.   

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Amazon VPC Route Server is now available in 8 new regions in addition to the 6 existing ones

VPC Route Server simplifies dynamic routing between virtual appliances in your Amazon VPC. It allows you to advertise routing information through Border Gateway Protocol (BGP) from virtual appliances and dynamically update the VPC route tables associated with subnets and internet gateway.

With this launch, Amazon VPC Route Server is available in 14 AWS Regions: US East (Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), US West (N. California), Canada West (Calgary), Asia Pacific (Malaysia), Europe (Milan), Europe (Paris), Asia Pacific (Sydney), Europe (London), Canada (Central). To learn more about Amazon VPC Route Server, visit this page.

 

​VPC Route Server simplifies dynamic routing between virtual appliances in your Amazon VPC. It allows you to advertise routing information through Border Gateway Protocol (BGP) from virtual appliances and dynamically update the VPC route tables associated with subnets and internet gateway. With this launch, Amazon VPC Route Server is available in 14 AWS Regions: US East (Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), US West (N. California), Canada West (Calgary), Asia Pacific (Malaysia), Europe (Milan), Europe (Paris), Asia Pacific (Sydney), Europe (London), Canada (Central). To learn more about Amazon VPC Route Server, visit this page.  

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Announcing AWS Builder Center

Today, we are announcing the launch of AWS Builder Center, a new place to bring together the global cloud community with everything needed to be successful in the cloud.

Anyone can now join the most experienced cloud community in the world. You can discover articles on trending topics, like and comment on impactful content, and share ideas with each other from an integrated experience. Learn about the communities or take part by joining a community program, such as AWS Community Builders, or start your own AWS User Group. You can also give feedback directly to AWS product teams through the new Wishlist feature and vote on community roadmap requests. Builder Center is the place to connect with AWS and the builder community.

Start with Builder Center and sign in with your AWS Builder ID. Don’t have a Builder ID? It’s easy to sign up, no credit card required. Once signed in, you can connect with fellow builders, interact with content, find events at the AWS Builder Loft, and access 600+ AWS Skill Builder Courses. If you’re looking to get hands-on, get started by downloading Q Developer, learn more about tools for different coding languages, or test your skills through a weekly technical challenge. You can display your contributions, and your community membership details on your public profile.

 Visit builder.aws.com to Start here. Go anywhere.

 

​Today, we are announcing the launch of AWS Builder Center, a new place to bring together the global cloud community with everything needed to be successful in the cloud.
Anyone can now join the most experienced cloud community in the world. You can discover articles on trending topics, like and comment on impactful content, and share ideas with each other from an integrated experience. Learn about the communities or take part by joining a community program, such as AWS Community Builders, or start your own AWS User Group. You can also give feedback directly to AWS product teams through the new Wishlist feature and vote on community roadmap requests. Builder Center is the place to connect with AWS and the builder community.
Start with Builder Center and sign in with your AWS Builder ID. Don’t have a Builder ID? It’s easy to sign up, no credit card required. Once signed in, you can connect with fellow builders, interact with content, find events at the AWS Builder Loft, and access 600+ AWS Skill Builder Courses. If you’re looking to get hands-on, get started by downloading Q Developer, learn more about tools for different coding languages, or test your skills through a weekly technical challenge. You can display your contributions, and your community membership details on your public profile.
 Visit builder.aws.com to Start here. Go anywhere.  

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Amazon QuickSight introduces granular access customization for exports and reports

Amazon QuickSight extends the current granular access capabilities with new options for exports and reports. QuickSight now allows administrators to customize access by export content type – PDF reports, CSV or Excel exports and dashboard prints for both on-demand exports on QuickSight Dashboards or Q visuals as well as scheduled email reports. Administrators can also limit attachments in report emails. Recipients will receive a notification only email with link to the actual generated content.

This set of capabilities provides administrators flexibility in managing their corporate policies on data exports for each user per export type.

Custom access capabilities in QuickSight can be applied at both user or role levels. To get started, see Customizing access to the Amazon QuickSight console.

 

​Amazon QuickSight extends the current granular access capabilities with new options for exports and reports. QuickSight now allows administrators to customize access by export content type – PDF reports, CSV or Excel exports and dashboard prints for both on-demand exports on QuickSight Dashboards or Q visuals as well as scheduled email reports. Administrators can also limit attachments in report emails. Recipients will receive a notification only email with link to the actual generated content. This set of capabilities provides administrators flexibility in managing their corporate policies on data exports for each user per export type. Custom access capabilities in QuickSight can be applied at both user or role levels. To get started, see Customizing access to the Amazon QuickSight console.