Amazon SageMaker AI now supports inference recommendations, a new capability that eliminates manual optimization and benchmarking to deliver optimal inference performance. By delivering validated, optimal deployment configurations with performance metrics, SageMaker AI accelerates the path to production and keeps your model developers focused on building accurate models, not managing infrastructure.

Customers bring their own generative AI models, define expected traffic patterns, and specify a performance goal (optimize for cost, minimize latency, or maximize throughput). SageMaker AI then analyzes the model’s architecture and applies optimizations aligned to that goal across multiple instance types, benchmarking each configuration on real GPU infrastructure using NVIDIA AIPerf. By evaluating multiple instance types, customers can select the most price-performant option for their workload. The result is deployment-ready configurations with validated metrics including time to first token, inter-token latency, request latency percentiles, throughput, and cost projections.

 The capability is available today in seven AWS Regions: US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Europe (Ireland), Asia Pacific (Singapore), and Europe (Frankfurt). To learn more, visit the SageMaker AI documentation.

​Amazon SageMaker AI now supports inference recommendations, a new capability that eliminates manual optimization and benchmarking to deliver optimal inference performance. By delivering validated, optimal deployment configurations with performance metrics, SageMaker AI accelerates the path to production and keeps your model developers focused on building accurate models, not managing infrastructure. Customers bring their own generative AI models, define expected traffic patterns, and specify a performance goal (optimize for cost, minimize latency, or maximize throughput). SageMaker AI then analyzes the model’s architecture and applies optimizations aligned to that goal across multiple instance types, benchmarking each configuration on real GPU infrastructure using NVIDIA AIPerf. By evaluating multiple instance types, customers can select the most price-performant option for their workload. The result is deployment-ready configurations with validated metrics including time to first token, inter-token latency, request latency percentiles, throughput, and cost projections.  The capability is available today in seven AWS Regions: US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Europe (Ireland), Asia Pacific (Singapore), and Europe (Frankfurt). To learn more, visit the SageMaker AI documentation.  

Today, AWS announced the availability of Qwen3-Coder-Next, Qwen3-30B-A3B, Qwen3-30B-A3B-Thinking-2507, Qwen3-Coder-30B-A3B-Instruct, and Qwen3.5-4B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These five models from Qwen bring specialized capabilities spanning agentic coding, efficient reasoning, extended thinking, and multimodal understanding, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure.

These models address different enterprise AI challenges with specialized capabilities:

Qwen3-Coder-Next excels at long-horizon reasoning, complex tool use, and recovery from execution failures, making it ideal for powering coding agents in CLI/IDE platforms.

Qwen3-30B-A3B uniquely supports seamless switching between thinking and non-thinking modes, making it well suited for general-purpose assistant tasks like multilingual dialogue, math reasoning, and tool calling.

Qwen3-30B-A3B-Thinking-2507 delivers significantly improved performance on complex reasoning tasks in math, science, and coding, with enhanced long-context understanding.

Qwen3-Coder-30B-A3B-Instruct is designed for agentic coding workflows with a custom function call format and repo-scale context understanding.

Qwen3.5-4B supports unified vision-language training and  201 languages, making it ideal for lightweight multimodal deployments.

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 Models section of SageMaker Studio 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.

​Today, AWS announced the availability of Qwen3-Coder-Next, Qwen3-30B-A3B, Qwen3-30B-A3B-Thinking-2507, Qwen3-Coder-30B-A3B-Instruct, and Qwen3.5-4B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These five models from Qwen bring specialized capabilities spanning agentic coding, efficient reasoning, extended thinking, and multimodal understanding, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3-Coder-Next excels at long-horizon reasoning, complex tool use, and recovery from execution failures, making it ideal for powering coding agents in CLI/IDE platforms.
Qwen3-30B-A3B uniquely supports seamless switching between thinking and non-thinking modes, making it well suited for general-purpose assistant tasks like multilingual dialogue, math reasoning, and tool calling.
Qwen3-30B-A3B-Thinking-2507 delivers significantly improved performance on complex reasoning tasks in math, science, and coding, with enhanced long-context understanding.
Qwen3-Coder-30B-A3B-Instruct is designed for agentic coding workflows with a custom function call format and repo-scale context understanding.
Qwen3.5-4B supports unified vision-language training and  201 languages, making it ideal for lightweight multimodal deployments.
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 Models section of SageMaker Studio 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.  

Amazon Elastic Kubernetes Service (EKS) now offers the Amazon EKS Hybrid Nodes gateway, a feature that automates networking between your Amazon EKS cluster VPC and Kubernetes Pods running on Amazon EKS Hybrid Nodes. The Amazon EKS Hybrid Nodes gateway eliminates the need to make on-premises pod networks routable or coordinate network infrastructure changes when running in hybrid Kubernetes environments.

Networking in hybrid Kubernetes environments can be complex, often requiring changes to on-premises routing configurations, coordination with network teams, and ongoing maintenance as workloads scale. The Amazon EKS Hybrid Nodes gateway addresses these challenges by automatically enabling Kubernetes control plane-to-webhook communication, pod-to-pod traffic across cloud and on-premises environments, and connectivity for AWS services such as Application Load Balancers, Network Load Balancers, and Amazon Managed Service for Prometheus. Customers deploy the Amazon EKS Hybrid Nodes gateway to Amazon EC2 instances using Helm, and the gateway automatically maintains VPC route tables as workloads scale. The Amazon EKS Hybrid Nodes gateway codebase is open source.

The Amazon EKS Hybrid Nodes gateway is available in all AWS Regions where Amazon EKS Hybrid Nodes is available, except the China Regions. The Amazon EKS Hybrid Nodes gateway is offered at no additional charge. You pay for the underlying AWS infrastructure used to run the gateway, including Amazon EC2 instance charges and any associated data transfer fees. To get started, visit the Amazon EKS Hybrid Nodes gateway documentation.

​Amazon Elastic Kubernetes Service (EKS) now offers the Amazon EKS Hybrid Nodes gateway, a feature that automates networking between your Amazon EKS cluster VPC and Kubernetes Pods running on Amazon EKS Hybrid Nodes. The Amazon EKS Hybrid Nodes gateway eliminates the need to make on-premises pod networks routable or coordinate network infrastructure changes when running in hybrid Kubernetes environments. Networking in hybrid Kubernetes environments can be complex, often requiring changes to on-premises routing configurations, coordination with network teams, and ongoing maintenance as workloads scale. The Amazon EKS Hybrid Nodes gateway addresses these challenges by automatically enabling Kubernetes control plane-to-webhook communication, pod-to-pod traffic across cloud and on-premises environments, and connectivity for AWS services such as Application Load Balancers, Network Load Balancers, and Amazon Managed Service for Prometheus. Customers deploy the Amazon EKS Hybrid Nodes gateway to Amazon EC2 instances using Helm, and the gateway automatically maintains VPC route tables as workloads scale. The Amazon EKS Hybrid Nodes gateway codebase is open source. The Amazon EKS Hybrid Nodes gateway is available in all AWS Regions where Amazon EKS Hybrid Nodes is available, except the China Regions. The Amazon EKS Hybrid Nodes gateway is offered at no additional charge. You pay for the underlying AWS infrastructure used to run the gateway, including Amazon EC2 instance charges and any associated data transfer fees. To get started, visit the Amazon EKS Hybrid Nodes gateway documentation.  

abril 21, 2026

• Categoría: Noticias de Microsoft

Cibertec y Microsoft se unen para fortalecer la formación técnica en habilidades digitales e inteligencia artificial en Perú

• Gracias a su alianza con Microsoft, Cibertec se posiciona entre los primeros institutos del país en integrar Microsoft 365 Copilot Chat en su experiencia formativa.

 

• Estudiantes y docentes podrán acceder a contenidos, capacitaciones y certificaciones oficiales de Microsoft.

Lima, Perú – En un contexto donde la inteligencia artificial está transformando el mundo del trabajo, Cibertec y Microsoft anuncian un convenio estratégico orientado a fortalecer la formación en inteligencia artificial y habilidades digitales, con el objetivo de preparar profesionales técnicos para el futuro. Con este acuerdo, Cibertec se convierte en uno de los primeros institutos del Perú en incorporar herramientas Microsoft 365 Copilot Chat como parte de su experiencia formativa, marcando un hito en la modernización de la educación técnica en el país.

Esta iniciativa responde a una tendencia global que impacta directamente en la empleabilidad. De acuerdo con el Future of Jobs Report 2025 del Foro Económico Mundial, hacia el año 2030 el 59% de la fuerza laboral deberá desarrollar nuevas habilidades, principalmente vinculadas a la inteligencia artificial, el análisis de datos y competencias digitales avanzadas, lo que evidencia la magnitud y urgencia del cambio en el mercado laboral.

En línea con el liderazgo que Cibertec ha construido en formación tecnológica orientada a la empleabilidad y, con una propuesta educativa diseñada para responder a las demandas reales del mercado laboral, Jaime Tamashiro, director general de Cibertec menciona: “La incorporación de tecnologías de clase mundial permitirá que los estudiantes se formen en entornos digitales similares a los que encontrarán en el mundo laboral, fortaleciendo competencias clave que hoy son altamente valoradas por los empleadores”, manifestó.

Por su parte Mario Rodríguez, Country Manager de Microsoft Perú detalla: “La inteligencia artificial está marcando un punto de inflexión en la forma en que trabajamos y aprendemos convirtiéndose en un habilitador central de productividad, creatividad e innovación en el mundo del trabajo. En este contexto, el desarrollo de habilidades digitales es clave para que los jóvenes puedan interactuar con la tecnología de manera crítica, responsable y efectiva, y estén mejor preparados para las oportunidades que demanda el mercado laboral actual y futuro. Por eso, la alianza que hoy firmamos con Cibertec representa un paso estratégico para acercar la inteligencia artificial al aula, integrar herramientas como Microsoft 365 Copilot Chat en la experiencia educativa y contribuir a la formación de talento preparado para impulsar el desarrollo del Perú”.

Como parte de esta alianza, se incorporarán de manera progresiva herramientas como Microsoft 365 Copilot Chat como punto de partida para la exploración y comprensión de la inteligencia artificial en la comunidad académica. El acuerdo contempla además programas de capacitación y certificación docente, así como la implementación de un Centro de Innovación Tecnológica, enfocado en la experimentación y aplicación de tecnologías digitales en educación.

La adopción de asistentes de inteligencia artificial por parte de los estudiantes se realizará de forma gradual, una vez que los docentes hayan sido capacitados, y siempre bajo criterios pedagógicos, regulatorios y éticos definidos por la institución, asegurando una implementación responsable y centrada en el aprendizaje.

Con esta iniciativa, Cibertec avanza en un modelo educativo que combina el talento humano y pedagógico con herramientas de inteligencia artificial, consolidándose como una institución de educación técnica orientada a formar talento preparado para desempeñarse en entornos cada vez más tecnológicos, dinámicos y competitivos.

La iniciativa se apoya en la trayectoria de ambas organizaciones en el fortalecimiento de capacidades digitales. Desde 2020, Microsoft ha capacitado a más de 130.000 personas en Perú en habilidades digitales, contribuyendo al desarrollo de talento preparado para un entorno laboral cada vez más tecnológico.

Acerca de Microsoft

Microsoft (Nasdaq «MSFT» @microsoft) crea plataformas y herramientas impulsadas por la IA para ofrecer soluciones innovadoras que satisfagan las necesidades cambiantes de nuestros clientes. La empresa de tecnología está comprometida con hacer que la IA esté ampliamente disponible, de manera responsable, con la misión de empoderar a cada persona y a cada organización en el planeta para lograr más.

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Amazon CloudWatch pipelines now lets you configure log processors using natural language descriptions powered by generative AI. CloudWatch pipelines is a fully managed service that ingests, transforms, and routes log data to CloudWatch without requiring you to manage infrastructure. Setting up the right combination of processors to parse and enrich logs can be time-consuming, especially when working with complex log formats. With AI-assisted configuration, you can simply describe the processing you need in plain language and have the pipeline configuration generated for you automatically.

When creating a pipeline in the CloudWatch console, toggle the AI-assisted option during the processing step and enter a natural language description of your desired transformations. The system generates the processor configuration along with a sample log event, so you can immediately verify the output before deploying. This reduces setup time and makes it easier to get your pipelines running correctly without needing deep familiarity with individual processor settings.

AI-assisted processor configuration is available at no additional cost in all AWS Regions where CloudWatch pipelines is generally available. Standard CloudWatch Logs ingestion and storage rates still apply.

To get started, open the Amazon CloudWatch console, navigate to pipelines under Ingestion, and follow the pipeline wizard. To learn more, see the CloudWatch pipelines documentation.

​Amazon CloudWatch pipelines now lets you configure log processors using natural language descriptions powered by generative AI. CloudWatch pipelines is a fully managed service that ingests, transforms, and routes log data to CloudWatch without requiring you to manage infrastructure. Setting up the right combination of processors to parse and enrich logs can be time-consuming, especially when working with complex log formats. With AI-assisted configuration, you can simply describe the processing you need in plain language and have the pipeline configuration generated for you automatically. When creating a pipeline in the CloudWatch console, toggle the AI-assisted option during the processing step and enter a natural language description of your desired transformations. The system generates the processor configuration along with a sample log event, so you can immediately verify the output before deploying. This reduces setup time and makes it easier to get your pipelines running correctly without needing deep familiarity with individual processor settings. AI-assisted processor configuration is available at no additional cost in all AWS Regions where CloudWatch pipelines is generally available. Standard CloudWatch Logs ingestion and storage rates still apply. To get started, open the Amazon CloudWatch console, navigate to pipelines under Ingestion, and follow the pipeline wizard. To learn more, see the CloudWatch pipelines documentation.  

Starting today, AWS Glue supports OAuth 2.0 authorization and authentication for native Snowflake connectivity, enabling customers to read from and write to Snowflake without sharing user credentials. This makes it easier for enterprises to maintain security compliance while building data integration pipelines. With OAuth support, you can now securely access Snowflake data within AWS Glue using temporary token-based authorization.

AWS Glue provides built-in connector to Snowflake, which helps you to integrate Snowflake data with other sources on a single platform while leveraging the scalability and performance of the AWS Glue Spark engine—all without installing or managing connector libraries. Previously, connecting to Snowflake required using persistent credentials or private keys. With OAuth 2.0 support, you can now eliminate credential management entirely, relying instead on secure, temporary tokens that enhance security and simplify access control. This approach enables granular access control, allowing you to define precise permissions for different users and applications. Additionally, token-based authentication provides improved auditability, making it easier to track and monitor data access patterns across your organization.

OAuth 2.0 support for AWS Glue’s Snowflake connector is available in all AWS commercial regions where AWS Glue is available.

To get started with configuring your AWS Glue Snowflake connection with OAuth, visit the AWS Glue documentation. 

​Starting today, AWS Glue supports OAuth 2.0 authorization and authentication for native Snowflake connectivity, enabling customers to read from and write to Snowflake without sharing user credentials. This makes it easier for enterprises to maintain security compliance while building data integration pipelines. With OAuth support, you can now securely access Snowflake data within AWS Glue using temporary token-based authorization. AWS Glue provides built-in connector to Snowflake, which helps you to integrate Snowflake data with other sources on a single platform while leveraging the scalability and performance of the AWS Glue Spark engine—all without installing or managing connector libraries. Previously, connecting to Snowflake required using persistent credentials or private keys. With OAuth 2.0 support, you can now eliminate credential management entirely, relying instead on secure, temporary tokens that enhance security and simplify access control. This approach enables granular access control, allowing you to define precise permissions for different users and applications. Additionally, token-based authentication provides improved auditability, making it easier to track and monitor data access patterns across your organization. OAuth 2.0 support for AWS Glue’s Snowflake connector is available in all AWS commercial regions where AWS Glue is available. To get started with configuring your AWS Glue Snowflake connection with OAuth, visit the AWS Glue documentation.   

Amazon SageMaker now supports multi-region replication from IAM Identity Center (IdC), enabling you to deploy SageMaker Unified Studio domains in different regions from your IdC instance. This new capability empowers enterprise customers, particularly those in regulated industries like financial services and healthcare, to maintain compliance while leveraging centralized workforce identity management.

As an Amazon SageMaker Unified Studio administrator, you can deploy SageMaker domains closer to your workforce based on data residency needs while maintaining seamless single sign-on (SSO) access. Organizations can address use cases such as maintaining IdC in one region while processing sensitive data in compliance-required regions, supporting global operations with centralized identity management, and meeting data sovereignty requirements without compromising SSO capabilities.

To get started see the SageMaker Unified Studio documentation and to learn about setting up IAM Identity Center multi-Region support see the IAM Identity Center User Guide.

​Amazon SageMaker now supports multi-region replication from IAM Identity Center (IdC), enabling you to deploy SageMaker Unified Studio domains in different regions from your IdC instance. This new capability empowers enterprise customers, particularly those in regulated industries like financial services and healthcare, to maintain compliance while leveraging centralized workforce identity management. As an Amazon SageMaker Unified Studio administrator, you can deploy SageMaker domains closer to your workforce based on data residency needs while maintaining seamless single sign-on (SSO) access. Organizations can address use cases such as maintaining IdC in one region while processing sensitive data in compliance-required regions, supporting global operations with centralized identity management, and meeting data sovereignty requirements without compromising SSO capabilities.
To get started see the SageMaker Unified Studio documentation and to learn about setting up IAM Identity Center multi-Region support see the IAM Identity Center User Guide.  

Amazon Athena Spark now supports AWS PrivateLink so that you can access APIs and endpoints from your Amazon Virtual Private Cloud (VPC) without traversing the public internet. This feature can help you meet compliance requirements by allowing you to access and use Athena Spark APIs and endpoints entirely within the AWS network.

You can now create AWS PrivateLink interface endpoints to connect from clients in your VPC. The Athena VPC endpoint supports all Athena Spark APIs and endpoints, including the Spark Connect, Spark Live UI and Spark History Server endpoints. Communication between your VPC and Athena Spark APIs and endpoints is then conducted entirely within the AWS network, providing a secure pathway for your data.

To get started, you can create an interface VPC endpoint to connect to Amazon Athena Spark using the AWS Management Console or AWS Command Line Interface (AWS CLI) commands or AWS CloudFormation. This new feature is available in all AWS Regions where Amazon Athena Spark and AWS PrivateLink are available. For more information, refer to the AWS PrivateLink documentation and Athena Spark documentation.

​Amazon Athena Spark now supports AWS PrivateLink so that you can access APIs and endpoints from your Amazon Virtual Private Cloud (VPC) without traversing the public internet. This feature can help you meet compliance requirements by allowing you to access and use Athena Spark APIs and endpoints entirely within the AWS network. You can now create AWS PrivateLink interface endpoints to connect from clients in your VPC. The Athena VPC endpoint supports all Athena Spark APIs and endpoints, including the Spark Connect, Spark Live UI and Spark History Server endpoints. Communication between your VPC and Athena Spark APIs and endpoints is then conducted entirely within the AWS network, providing a secure pathway for your data. To get started, you can create an interface VPC endpoint to connect to Amazon Athena Spark using the AWS Management Console or AWS Command Line Interface (AWS CLI) commands or AWS CloudFormation. This new feature is available in all AWS Regions where Amazon Athena Spark and AWS PrivateLink are available. For more information, refer to the AWS PrivateLink documentation and Athena Spark documentation.
   

AWS Transform custom is now available in six additional AWS Regions: Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (London).

AWS Transform custom enables organizations to modernize and transform code at scale using AWS-managed and custom transformations. You can upgrade language versions, migrate frameworks, optimize performance, and analyze code bases using transformations that are ready to use or can be customized to meet your organization’s specific requirements. These transformations benefit from continuous improvement, learning from each engagement to deliver increasingly accurate and efficient results.

With this expansion, AWS Transform custom is now available in a total of eight AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (Frankfurt, London). To learn more, visit the AWS Transform product page and user guide.

​AWS Transform custom is now available in six additional AWS Regions: Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (London). AWS Transform custom enables organizations to modernize and transform code at scale using AWS-managed and custom transformations. You can upgrade language versions, migrate frameworks, optimize performance, and analyze code bases using transformations that are ready to use or can be customized to meet your organization’s specific requirements. These transformations benefit from continuous improvement, learning from each engagement to deliver increasingly accurate and efficient results.
With this expansion, AWS Transform custom is now available in a total of eight AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (Frankfurt, London). To learn more, visit the AWS Transform product page and user guide.  

AWS Backup now supports Amazon Redshift Serverless namespaces and Amazon Aurora DSQL clusters as resource types in AWS Organizations backup policies. Organization administrators can now define backup policy rules that directly target these resource types across member accounts.

Previously, backing up Redshift Serverless namespaces and Aurora DSQL clusters through organization backup policies required using tag-based selections or backing up all resources in a member account. With this launch, administrators can specify these resource types directly in their backup policy selections, providing more precise control over which resources are included in or excluded from Organization-wide backup plans.

This capability is available in all AWS Commercial and GovCloud Regions where AWS Backup and the respective services are available. To get started, visit the AWS Organizations backup policies documentation or the AWS Backup console.

​AWS Backup now supports Amazon Redshift Serverless namespaces and Amazon Aurora DSQL clusters as resource types in AWS Organizations backup policies. Organization administrators can now define backup policy rules that directly target these resource types across member accounts.
Previously, backing up Redshift Serverless namespaces and Aurora DSQL clusters through organization backup policies required using tag-based selections or backing up all resources in a member account. With this launch, administrators can specify these resource types directly in their backup policy selections, providing more precise control over which resources are included in or excluded from Organization-wide backup plans.
This capability is available in all AWS Commercial and GovCloud Regions where AWS Backup and the respective services are available. To get started, visit the AWS Organizations backup policies documentation or the AWS Backup console.