Amazon Connect Customer now enables agents to bid on preferred shifts, giving them more control over their work schedules. Schedulers first establish agent ranking either by uploading a CSV file or generating a randomized ranking. Connect Customer then uses forecasted demand and shift profiles to generate available shifts and presents them to agents to rank. For example, in a Monday–Friday 6AM–10PM shift profile with 9-hour shifts, required shifts are 6AM–3PM (500 agents), 9AM–6PM (800 agents), and 1PM–10PM (600 agents). Once the bidding window closes, Connect Customer automatically assigns each agent to their highest-ranked available shift while using agent ranking as a tiebreaker. Shift bidding gives agents a structured way to influence their own schedules, while reducing the time schedulers spend on manual shift assignments, improving both agent satisfaction and scheduling efficiency.
This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here.
Amazon Connect Customer now enables agents to bid on preferred shifts, giving them more control over their work schedules. Schedulers first establish agent ranking either by uploading a CSV file or generating a randomized ranking. Connect Customer then uses forecasted demand and shift profiles to generate available shifts and presents them to agents to rank. For example, in a Monday–Friday 6AM–10PM shift profile with 9-hour shifts, required shifts are 6AM–3PM (500 agents), 9AM–6PM (800 agents), and 1PM–10PM (600 agents). Once the bidding window closes, Connect Customer automatically assigns each agent to their highest-ranked available shift while using agent ranking as a tiebreaker. Shift bidding gives agents a structured way to influence their own schedules, while reducing the time schedulers spend on manual shift assignments, improving both agent satisfaction and scheduling efficiency.
This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here.
AWS root user sign-in is now served across US East (N. Virginia), US East (Ohio), and US West (Oregon), with sign-in traffic distributed across all three Regions. This change reduces reliance on US East (N. Virginia) and improves resiliency during service disruptions. AWS automatically routes your root user sign-in to a supported Region without requiring you to select a Region or change how you sign in. This improvement is available now for all AWS accounts.
In AWS CloudTrail, ConsoleLogin events for root user sign-ins are recorded in the Region that processed the sign-in request. To maintain full visibility into root user sign-in activity, update your monitoring and alerting to cover US East (N. Virginia), US East (Ohio), and US West (Oregon).
AWS root user sign-in is now served across US East (N. Virginia), US East (Ohio), and US West (Oregon), with sign-in traffic distributed across all three Regions. This change reduces reliance on US East (N. Virginia) and improves resiliency during service disruptions. AWS automatically routes your root user sign-in to a supported Region without requiring you to select a Region or change how you sign in. This improvement is available now for all AWS accounts.
In AWS CloudTrail, ConsoleLogin events for root user sign-ins are recorded in the Region that processed the sign-in request. To maintain full visibility into root user sign-in activity, update your monitoring and alerting to cover US East (N. Virginia), US East (Ohio), and US West (Oregon).
To learn more, see the AWS Sign-In documentation and the CloudTrail ConsoleLogin event reference.
Ayudar a familias y educadores a apoyar experiencias más seguras en Windows
Por: Rob Mauceri, ingeniero distinguido, Seguridad Digital de Windows.
Nuestros niños y adolescentes crecen en un mundo digital donde las aplicaciones, juegos y herramientas impulsadas por IA forman parte de cómo aprenden, juegan, crean y se conectan. Para padres y educadores, eso aporta tanto oportunidad como responsabilidad: las experiencias digitales deben ayudar a los jóvenes a explorar con confianza mientras apoyan su bienestar, establecen hábitos saludables, respetan su privacidad y ofrecen protecciones adecuadas a su edad.
En Microsoft, creemos que la seguridad debería ser más fácil de entender para las familias y más fácil para que los desarrolladores integren experiencias que los niños utilicen. En Windows, eso empieza por la cuenta. La cuenta de Microsoft es una base de confianza a través de la cual Windows aporta protecciones y decisiones parentales adecuadas a la edad a más experiencias digitales que viven niños y adolescentes cada día.
La cuenta de un niño debería ayudar a desbloquear experiencias más seguras y adecuadas para la edad dondequiera que use Windows.
Esta es la primera de una serie de publicaciones sobre la seguridad familiar en Windows. En los próximos meses compartiré lo que construimos — en la cuenta, en la plataforma y en las herramientas que ofrecemos a padres, educadores y desarrolladores. Como Pavan compartió de manera reciente, la familia es un área clave en nuestro compromiso con mejorar la calidad de Windows. Construiremos a la luz de todos: lanzaremos nuevas capacidades, escucharemos con atención lo que nos dicen las familias y perfeccionaremos a medida que aprendemos.
De la cuenta a experiencias más seguras
Toda experiencia en Windows comienza con la identidad, y para los consumidores, esa base es la cuenta de Microsoft (MSA, por sus siglas en inglés).
MSA permite a Windows comprender el contexto clave sobre el usuario de manera coherente y respetuosa con la privacidad:
Ya sea niño, adolescente o adulto
Ya sea que se apliquen controles parentales o consentimiento
Si se ha completado la verificación de edad
Esto crea un modelo sencillo y escalable:
Cuenta de usuario → señal de edad → experiencia y controles apropiados para la edad
Dado que esta base está ligada a la cuenta, las protecciones están diseñadas para acompañar al usuario a través de Windows y aplicaciones, con la creación de experiencias más seguras de manera consistente, no solo en características individuales. Esto permite que las experiencias de producto apoyen la etapa de vida del usuario. Las protecciones se adaptan a niños, adolescentes y adultos.
Ampliar la seguridad a todo el ecosistema con las APIs de Windows Age
A medida que las experiencias se expanden a través de aplicaciones, servicios e IA, queda claro un reto: ¿cómo pueden estas experiencias comprender y respetar de manera consistente la edad de un usuario?
Para abordar esto, Windows introduce una nueva capacidad de plataforma: la API Windows Age.
Esta API aporta la conciencia sobre la edad más allá del sistema operativo al hacerla disponible en todo el ecosistema de Windows, de modo que las aplicaciones y servicios puedan ofrecer experiencias adecuadas a la edad utilizando la misma base de confianza. Al poner a disposición la concienciación sobre la edad como una capacidad de plataforma, Windows ayuda a los desarrolladores a crear salvaguardas en las experiencias desde el principio, en lugar de cargar la carga sobre niños y familias para gestionar las protecciones aplicación por aplicación.
La API de Windows Age ofrece las siguientes capacidades:
GetUserAgeRangeAsync: Proporciona categorías de grupos de edad no identificables de manera personal, para que las aplicaciones las utilicen para ofrecer experiencias de usuario seguras y adecuadas a su edad, al proteger los datos de fecha de nacimiento y la privacidad general de los usuarios. Los grupos de edad disponibles a través de la API incluyen: menores de 10, 10-12, 13-15, 16-17, 18+.
GetAgeVerificationStatusAsync: Proporciona estado de edad verificado para escenarios en los que la edad auto-reportada no es suficiente.
CheckAgeStatusAsync: Extiende la funcionalidad de la antigua API CheckUserAgeConsentGroupAsync para ofrecer clasificación de niños, menores o adultos a nivel global y según lo definido por las políticas regionales.
Estas señales están diseñadas para proteger la privacidad:
Las aplicaciones solo reciben la señal relacionada con la edad necesaria para la experiencia y no datos personales sensibles como la fecha completa de nacimiento
Los usuarios mantienen el control mediante el consentimiento y controles a nivel de plataforma
La información de edad se gestiona de manera coherente a nivel de plataforma para reducir el intercambio innecesario y la fragmentación
Esto permite un nuevo nivel de coherencia entre las experiencias de Windows. Las aplicaciones pueden reconocer cuándo un usuario es menor de cierta edad y ajustar las características en consecuencia, lo que ayuda a crear interacciones más seguras y adecuadas sin necesidad de configuración manual. Este enfoque de plataforma también ayuda a que las protecciones sean más consistentes y accesibles, de modo que las experiencias más seguras no dependan de si una familia descubre o configura controles en cada aplicación individual.
Por igual importante, esto se extiende a experiencias emergentes, incluida la IA, donde aplicar salvaguardas adecuadas a la edad es cada vez más importante a medida que la innovación sigue evolucionando.
Al convertir esto en una capacidad de plataforma, Windows fomenta una seguridad integrada y disponible en aplicaciones, servicios y nuevos escenarios.
Para más detalles, incluida la orientación para desarrolladores, consulten la documentación de la API de Windows Age que se encuentra en https://aka.ms/windows-age-api.
Construir hacia una garantía de edad con alta confianza
En muchos casos, entender la edad no es suficiente. Cada vez más, ciertas experiencias requieren una alta confianza en que el usuario es adulto.
Para apoyar esto, Microsoft amplía la garantía de edad a través de la cuenta Microsoft utilizando Microsoft Age Verification (MAV), una plataforma centralizada que permite a los usuarios verificar su edad una vez y usar ese estado en las experiencias de Microsoft. Los desarrolladores pueden acceder a este estado verificado a través de las APIs de Windows Age.
La verificación de edad ya está disponible en Microsoft Storefronts en Singapur, Brasil y Australia. A medida que más regiones del mundo amplían los requisitos regulatorios para la verificación de edad, Microsoft añadirá soporte para esas geografías y escenarios de productos.
MAV crea un modelo simple y consistente:
Verificar una vez → usarlo en todas partes
El estado verificado se almacena con la cuenta de Microsoft y puede usarse en Windows y aplicaciones para ofrecer experiencias adecuadas, lo que ayuda a reducir fricciones y mejorar la seguridad.
Dejar la seguridad más clara desde el principio
También mejoramos cómo se manifiestan los controles parentales durante la configuración del dispositivo, para que las familias puedan entender más fácil las protecciones disponibles y tomar decisiones informadas desde el principio.
En regiones como Francia y otras, Windows aumenta la visibilidad de los controles parentales y las experiencias de consentimiento durante la configuración, diseñada para aumentar la conciencia sobre las ofertas disponibles y permitir la aplicación temprana por parte de los padres.
Seguridad familiar y controles parentales
Con la Seguridad Familiar integrada en Windows, los padres pueden configurar el dispositivo de un niño y gestionar el tiempo frente a pantallas, las aplicaciones y el contenido en un solo lugar. Durante los últimos seis meses, nos hemos centrado en hacer que esos momentos cotidianos sean más fiables: aprobaciones parentales más rápidas, informes de actividades más claros y mayor control general, guiados por lo que los padres nos dijeron que era lo más importante. Pueden leer más sobre lo que ha mejorado y lo que viene a continuación aquí: Escuchar a las familias. Mejorar Microsoft Family.
Elevar el listón de la seguridad digital
La seguridad digital debe estar integrada en la plataforma, no añadirse más adelante.
Con la cuenta de Microsoft como base, e inversiones como la ampliación de la garantía de edad de la API Windows Age y controles parentales disponibles, Windows trabaja para apoyar un entorno digital más seguro que también sea adecuado para la edad, respetuoso con la privacidad y más fácil de navegar para las familias.
Nuestro objetivo es ayudar a crear experiencias en las que la seguridad, la privacidad y la participación puedan trabajar juntas en todo el ecosistema Windows.
Disponibilidad
Las APIs de Windows Age están ahora disponibles de manera amplia para Windows Insiders y pronto estarán disponibles para todos los usuarios de Windows. La API CheckAgeStatusAsync estará disponible con una actualización futura.
NVIDIA’s Qwen3.6-35B-A3B-NVFP4 and Alibaba’s Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba’s Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA’s ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint.
Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
NVIDIA’s Qwen3.6-35B-A3B-NVFP4 and Alibaba’s Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba’s Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA’s ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint.
Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Mistral AI’s Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license.
Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Mistral AI’s Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license.
Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Google DeepMind’s Gemma-4-31B-it-assistant and NVIDIA’s Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring the flagship Gemma 4 31B dense architecture to enterprise workloads in both full-precision and optimized quantized variants, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Gemma-4-31B-it-assistant is built for multimodal reasoning, coding, and agentic workflows as the assistant-tuned variant of Google’s flagship 31B dense model. It handles text and image inputs (including video as frame sequences) and generates text output, with a 256K-token context window and support for over 140 languages. Ranked #3 among open models on the Arena AI text leaderboard—outcompeting models 20x its size—it features a hybrid attention mechanism interleaving local sliding-window and full global attention with native function calling for building autonomous agents.
Gemma-4-31B-IT-NVFP4 delivers the same Gemma 4 31B capabilities at a fraction of the memory footprint. Quantized with NVIDIA’s ModelOpt framework to 4-bit FP4 precision, it reduces memory usage to ~18.5 GB (68% smaller than the base model) and achieves approximately 2.5x faster inference while retaining 97–99% of the original model’s quality. Ideal for cost-efficient, high-throughput production deployments on NVIDIA RTX, DGX Spark, and data center GPUs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Google DeepMind’s Gemma-4-31B-it-assistant and NVIDIA’s Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring the flagship Gemma 4 31B dense architecture to enterprise workloads in both full-precision and optimized quantized variants, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Gemma-4-31B-it-assistant is built for multimodal reasoning, coding, and agentic workflows as the assistant-tuned variant of Google’s flagship 31B dense model. It handles text and image inputs (including video as frame sequences) and generates text output, with a 256K-token context window and support for over 140 languages. Ranked #3 among open models on the Arena AI text leaderboard—outcompeting models 20x its size—it features a hybrid attention mechanism interleaving local sliding-window and full global attention with native function calling for building autonomous agents.
Gemma-4-31B-IT-NVFP4 delivers the same Gemma 4 31B capabilities at a fraction of the memory footprint. Quantized with NVIDIA’s ModelOpt framework to 4-bit FP4 precision, it reduces memory usage to ~18.5 GB (68% smaller than the base model) and achieves approximately 2.5x faster inference while retaining 97–99% of the original model’s quality. Ideal for cost-efficient, high-throughput production deployments on NVIDIA RTX, DGX Spark, and data center GPUs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
BM’s granite-speech-4.1-2b, Kakao’s kanana-2-30b-a3b-instruct, and the OpenFold Consortium’s OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning multilingual speech recognition, bilingual agentic AI, and biomolecular structure prediction, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
granite-speech-4.1-2b is purpose-built for multilingual automatic speech recognition (ASR) and bidirectional speech translation (AST) across English, French, German, Spanish, Portuguese, and Japanese. This compact 2B-parameter speech-language model delivers a word error rate of 5.33% with a real-time factor of ~231, making it one of the most efficient ASR models in its class. Released under Apache 2.0, it integrates seamlessly into enterprise voice workflows for transcription, translation, and audio processing at scale.
kanana-2-30b-a3b-instruct excels in bilingual Korean-English instruction following and agentic AI workflows. Developed by Kakao, it adopts a cutting-edge architecture featuring Multi-head Latent Attention (MLA) and Mixture-of-Experts (MoE), activating only 3B of its 30B total parameters per forward pass for superior throughput. Post-trained with supervised fine-tuning and reinforcement learning, it supports up to 128K tokens via YaRN scaling and is designed to function as an AI collaborator that understands context and acts proactively.
OpenFold3 provides all-atom biomolecular complex structure prediction for proteins, DNA, RNA, and small-molecule ligands. Developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University, this diffusion-based model extends structure prediction beyond single proteins to model multi-chain complexes and heterogeneous biomolecular interactions. It supports computer-aided drug design and is applicable across academic and pharmaceutical research labs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
BM’s granite-speech-4.1-2b, Kakao’s kanana-2-30b-a3b-instruct, and the OpenFold Consortium’s OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning multilingual speech recognition, bilingual agentic AI, and biomolecular structure prediction, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
granite-speech-4.1-2b is purpose-built for multilingual automatic speech recognition (ASR) and bidirectional speech translation (AST) across English, French, German, Spanish, Portuguese, and Japanese. This compact 2B-parameter speech-language model delivers a word error rate of 5.33% with a real-time factor of ~231, making it one of the most efficient ASR models in its class. Released under Apache 2.0, it integrates seamlessly into enterprise voice workflows for transcription, translation, and audio processing at scale.
kanana-2-30b-a3b-instruct excels in bilingual Korean-English instruction following and agentic AI workflows. Developed by Kakao, it adopts a cutting-edge architecture featuring Multi-head Latent Attention (MLA) and Mixture-of-Experts (MoE), activating only 3B of its 30B total parameters per forward pass for superior throughput. Post-trained with supervised fine-tuning and reinforcement learning, it supports up to 128K tokens via YaRN scaling and is designed to function as an AI collaborator that understands context and acts proactively.
OpenFold3 provides all-atom biomolecular complex structure prediction for proteins, DNA, RNA, and small-molecule ligands. Developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University, this diffusion-based model extends structure prediction beyond single proteins to model multi-chain complexes and heterogeneous biomolecular interactions. It supports computer-aided drug design and is applicable across academic and pharmaceutical research labs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Presentamos Azure Multicloud Interconnect para AWS
Por: Narayan Annamalai, vicepresidente y jefe de productos de Azure Networking Services.
A medida que las organizaciones aceleran la adopción de la IA y modernizan sus entornos digitales, las aplicaciones, datos e infraestructuras abarcan cada vez más múltiples entornos en la nube. Los clientes eligen la mejor plataforma para cada carga de trabajo, para aprovechar capacidades únicas de los proveedores y así impulsar la innovación, la resiliencia y la agilidad empresarial.
Sin embargo, aunque las estrategias de nube múltiple se han vuelto habituales, la red entre entornos de nube es todavía compleja. Establecer conectividad privada suele requerir que los clientes unan de manera manual los servicios, coordinen el aprovisionamiento entre proveedores, gestionen múltiples procesos operativos y naveguen por experiencias de soporte fragmentadas. Lo que debería ser una decisión sencilla de conectividad puede llevar semanas o meses en implementarse y gestionarse.
Hoy en día, Microsoft y Amazon Web Services (AWS) dan un paso importante para simplificar esa experiencia.
Microsoft Azure y AWS están entusiasmados por colaborar en una solución de red de nube múltiple que utilice tanto AWS Interconnect – multicloud como Azure Multicloud Interconnect para la interoperabilidad de red, lo que permite a los clientes establecer una conectividad privada y de alto rendimiento entre Microsoft Azure y AWS mediante una experiencia simplificada y nativa de la nube.
Esta colaboración se construye a través de las especificaciones Open API para la interoperabilidad de la red. Azure Multicloud Interconnect ayuda a eliminar gran parte de la complejidad asociada de manera tradicional a las redes de nube múltiple. Los clientes pueden proporcionar conectividad a través de una experiencia integrada mientras se benefician de rendimiento, resiliencia, seguridad y simplicidad operativa de nivel empresarial.
Simplificación de la conectividad multicloud
Hasta ahora, las organizaciones que conectaban entornos Azure y AWS requerían una planificación cuidadosa, conectividad física, configuración de enrutamiento, coordinación de provisionamiento, monitorización y gestión del ciclo de vida para ensamblar y mantener múltiples componentes entre proveedores.
Azure Multicloud Interconnect cambia de manera fundamental este modelo. Con la colaboración de Microsoft y AWS para utilizar la especificación estandarizada de Open API, los clientes pueden establecer conectividad privada dedicada mediante una experiencia simplificada que abstrae la complejidad subyacente de las redes multicloud. En lugar de centrarse en la gestión de infraestructuras, las organizaciones pueden centrarse en entregar aplicaciones, mover datos y acelerar los resultados empresariales.
El resultado es un camino de alto ancho de banda y más predecible para desplegar arquitecturas de nube múltiple para cargas de trabajo críticas para la misión.
Diseñado para la era de la IA
Las cargas de trabajo de entrenamiento e inferencia requieren con frecuencia de acceso a datos distribuidos entre entornos. Las empresas diseñan cada vez más aplicaciones que cruzan los límites de la nube para mantener los requisitos de rendimiento, seguridad y cumplimiento.
Azure Multicloud Interconnect está diseñado para soportar estos requisitos en evolución con una conectividad privada de alta capacidad que se extiende hasta Azure Private Link, para brindar un camino privado de extremo a extremo entre las nubes.
Esta combinación de conectividad de alto rendimiento y simplicidad operativa permite a los clientes avanzar más rápido mientras desarrollan la próxima generación de aplicaciones y servicios habilitados por IA.
A medida que la IA transforma todos los sectores, los clientes necesitan la libertad de colocar datos, aplicaciones e infraestructura donde sea que aporte el mayor valor empresarial. Azure Multicloud Interconnect ayuda a hacerlo posible al proporcionar una conectividad privada resiliente y de alto rendimiento entre Azure y AWS mediante una experiencia simplificada y nativa en la nube. Juntos, redujimos la complejidad de las redes de nube múltiple y proporcionamos a los clientes la escala, fiabilidad y agilidad que necesitan para impulsar la próxima generación de innovación basada en IA y datos.
Los clientes nos dijeron que querían una mejor manera de conectar cargas de trabajo que abarcan AWS y Azure, y las formas antiguas de hacerlo eran torpes. Con AWS Interconnect-multicloud y Azure Multicloud Interconnect, demostramos lo que es posible cuando ambas partes se comprometen con un listón muy alto: seguridad MACsec desde el primer momento, disponibilidad de cuatro nueves y escalabilidad con solo pulsar un botón.
—Robert Kennedy, vicepresidente de Servicios de Red en AWS
Avanzar hacia un futuro de nube múltiple abierta
Azure Multicloud Interconnect representa más que una nueva oferta de conectividad: es un paso hacia un ecosistema de nube más abierto e interconectado.
De cara al futuro, vemos la oportunidad de ampliar este modelo más allá de una relación nube a nube. La misma especificación abierta de API puede ayudar a permitir una interoperabilidad más amplia entre proveedores de nube hiperescaladas, para crear una experiencia más coherente para los clientes que operan en entornos de nube múltiple cada vez más diversos.
Los clientes pueden desplegar conectividad a velocidades de hasta 100 Gbps desde el primer día en disponibilidad general, lo que ayuda a satisfacer las necesidades de aplicaciones de alto ancho de banda y proporciona la base para el crecimiento futuro. A medida que aumenta la demanda, la capacidad puede expandirse de manera dinámica, lo que permite a las organizaciones escalar sin interrumpir las operaciones ni rediseñar la arquitectura de su red.
Más allá de los hiperescaladores, este enfoque tiene el potencial de simplificar la conectividad con los proveedores de servicios de red y los operadores de telecomunicaciones. Al adoptar un modelo común de interoperabilidad, los proveedores de la nube y los operadores pueden trabajar juntos para agilizar la conectividad de última milla, acelerar el aprovisionamiento y reducir la complejidad operativa a lo largo del recorrido integral del cliente.
Nuestra visión a largo plazo es un ecosistema abierto donde los hiperescaladores, proveedores de servicios de red, y operadores de telecomunicaciones utilicen un marco común de interoperabilidad para establecer y operar la conectividad mediante APIs estandarizadas. Los clientes deberían poder proporcionar conectividad confiable y de alto rendimiento entre nubes, redes metropolitanas y ubicaciones empresariales con la misma simplicidad y automatización que esperan de los servicios modernos en la nube.
AWS Elemental MediaLive now supports Video Aligned Locking, a new feature to synchronize video pipelines without requiring timecode from the source. Previously, achieving frame-accurate locking across video outputs required investing in specialized hardware or managing complex, external synchronization workflows. While timecode is generally standard for highly produced, traditional broadcast content, it is often unavailable and difficult to manage in typical digital streaming workflows. The new capability uses visual signatures to automatically identify and align specific frames across multiple video streams, enabling customers to achieve frame-accurate input switching across standard pipeline channels, as well as linked, cross-region single-pipeline channels.
Video Aligned Locking supports outputs including HLS, MediaPackage, CMAF Ingest, UDP and SRT. To learn more about how to configure this feature, visit the Requirements for Video Aligned Locking documentation. For a deeper dive into channel synchronization, visit the guide on Implementing Pipeline Locking.
AWS Elemental MediaLive now supports Video Aligned Locking, a new feature to synchronize video pipelines without requiring timecode from the source. Previously, achieving frame-accurate locking across video outputs required investing in specialized hardware or managing complex, external synchronization workflows. While timecode is generally standard for highly produced, traditional broadcast content, it is often unavailable and difficult to manage in typical digital streaming workflows. The new capability uses visual signatures to automatically identify and align specific frames across multiple video streams, enabling customers to achieve frame-accurate input switching across standard pipeline channels, as well as linked, cross-region single-pipeline channels.
Video Aligned Locking supports outputs including HLS, MediaPackage, CMAF Ingest, UDP and SRT. To learn more about how to configure this feature, visit the Requirements for Video Aligned Locking documentation. For a deeper dive into channel synchronization, visit the guide on Implementing Pipeline Locking.
AWS Lambda now supports direct read configuration for Amazon S3 Files, letting you configure which storage your functions read from: S3 Files high-performance storage or your S3 bucket. With this launch, you can optimize the throughput and latency of file reads for your Lambda functions based on your application requirements.
Customers use S3 Files with Lambda functions to build scalable data processing pipelines and stateful agentic workloads with the performance and simplicity of a file system, while benefiting from the scalability, durability, and cost-effectiveness of S3. S3 Files serves data from high-performance storage for low latency or directly from your S3 bucket for high throughput on large reads, automatically routing each operation to the storage best suited for it. By default, Lambda supports direct reads from your S3 bucket only for functions configured with 512 MB of memory or higher. However, without control over the direct read configuration, you cannot optimize read performance for your specific application requirements. With this launch, you can explicitly enable or disable direct read for S3 Files on your Lambda functions, independent of function memory size. When you enable direct read, your function reads files 1 MB or larger directly from your S3 bucket for maximum throughput, and smaller files are served through the high-performance storage. When you disable it, all reads are served through the high-performance storage for the lowest latency.
This capability is available in all AWS commercial Regions, AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions, except Asia Pacific (New Zealand), Middle East (Bahrain), and Middle East (UAE). You can configure direct read for S3 Files using the AWS Management Console, AWS CLI, AWS SDKs, and AWS CloudFormation. There is no additional charge beyond standard Lambda and S3 Files pricing. To learn more about how to use S3 Files with your Lambda function, visit the AWS Lambda developer guide.
AWS Lambda now supports direct read configuration for Amazon S3 Files, letting you configure which storage your functions read from: S3 Files high-performance storage or your S3 bucket. With this launch, you can optimize the throughput and latency of file reads for your Lambda functions based on your application requirements.
Customers use S3 Files with Lambda functions to build scalable data processing pipelines and stateful agentic workloads with the performance and simplicity of a file system, while benefiting from the scalability, durability, and cost-effectiveness of S3. S3 Files serves data from high-performance storage for low latency or directly from your S3 bucket for high throughput on large reads, automatically routing each operation to the storage best suited for it. By default, Lambda supports direct reads from your S3 bucket only for functions configured with 512 MB of memory or higher. However, without control over the direct read configuration, you cannot optimize read performance for your specific application requirements. With this launch, you can explicitly enable or disable direct read for S3 Files on your Lambda functions, independent of function memory size. When you enable direct read, your function reads files 1 MB or larger directly from your S3 bucket for maximum throughput, and smaller files are served through the high-performance storage. When you disable it, all reads are served through the high-performance storage for the lowest latency.
This capability is available in all AWS commercial Regions, AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions, except Asia Pacific (New Zealand), Middle East (Bahrain), and Middle East (UAE). You can configure direct read for S3 Files using the AWS Management Console, AWS CLI, AWS SDKs, and AWS CloudFormation. There is no additional charge beyond standard Lambda and S3 Files pricing. To learn more about how to use S3 Files with your Lambda function, visit the AWS Lambda developer guide.
