AWS Transform is now available through two additional developer tools — including Kiro and VS Code. AWS Transform is an agentic migration and modernization factory designed to compress enterprise transformation timelines from years to months — handling everything from large-scale infrastructure migrations to continuous tech debt reduction, without the manual handoffs and lost context that commonly stall these programs..

With today’s launch, you can get started with AWS Transform custom transformations from wherever you already work: install the AWS Transform Power in Kiro, or install the AWS Transform extension in VS Code . AWS Transform custom transformations help you crush tech debt at scale — choose from AWS-managed transformations for common patterns like Java, Python, and Node.js version upgrades, AWS SDK migrations (boto2 to boto3, Java SDK v1 to v2, JS SDK v2 to v3), or define your own. These new surfaces make it easier to discover additional capabilities as they become available, build and iterate on your own custom transformations, and run any agent repeatedly or across thousands of repositories at once. The custom transformations are the first in a growing library of playbooks coming to developer tools, complementing the existing AWS Transform web console and CLI so you can start a job in your IDE, track progress in the web console, and finish transformations wherever it makes sense — with job state and context shared across every surface.

AWS Transform supports deploying to all AWS commercial regions,and AWS Transform custom is available in US East (N. Virginia) and Europe (Frankfurt). To learn more, visit the AWS Transform product page and user guide.

​AWS Transform is now available through two additional developer tools — including Kiro and VS Code. AWS Transform is an agentic migration and modernization factory designed to compress enterprise transformation timelines from years to months — handling everything from large-scale infrastructure migrations to continuous tech debt reduction, without the manual handoffs and lost context that commonly stall these programs..
With today’s launch, you can get started with AWS Transform custom transformations from wherever you already work: install the AWS Transform Power in Kiro, or install the AWS Transform extension in VS Code . AWS Transform custom transformations help you crush tech debt at scale — choose from AWS-managed transformations for common patterns like Java, Python, and Node.js version upgrades, AWS SDK migrations (boto2 to boto3, Java SDK v1 to v2, JS SDK v2 to v3), or define your own. These new surfaces make it easier to discover additional capabilities as they become available, build and iterate on your own custom transformations, and run any agent repeatedly or across thousands of repositories at once. The custom transformations are the first in a growing library of playbooks coming to developer tools, complementing the existing AWS Transform web console and CLI so you can start a job in your IDE, track progress in the web console, and finish transformations wherever it makes sense — with job state and context shared across every surface.
AWS Transform supports deploying to all AWS commercial regions,and AWS Transform custom is available in US East (N. Virginia) and Europe (Frankfurt). To learn more, visit the AWS Transform product page and user guide.  

AWS Secrets Manager now supports hybrid post-quantum key exchange using ML-KEM (Module-Lattice-based Key-Encapsulation Mechanism) to secure TLS connections for retrieving and managing secrets. This protection is automatically enabled in Secrets Manager Agent (version 2.0.0+), AWS Lambda Extension (version 19+), and Secrets Manager CSI Driver (version 2.0.0+). For SDK-based clients, hybrid post-quantum key exchange is available in supported AWS SDKs including Rust, Go, Node.js, Kotlin, Python (with OpenSSL 3.5+), and Java v2 (v2.35.11+).

With this launch, your applications retrieve secrets over TLS connections that combine classical key exchange with post-quantum cryptography, helping protect against both traditional cryptographic attacks and future quantum computing threats known as «harvest now, decrypt later» (HNDL). No code changes, configuration updates, or migration effort are required for customers using the latest client versions except for Java v2. For example, a microservice requiring multiple secrets at startup can now retrieve them over quantum-resistant TLS connections by simply upgrading to the latest Secrets Manager Agent version. You can verify hybrid post-quantum key exchange is active by checking CloudTrail logs for the «X25519MLKEM768» key exchange algorithm in the tlsDetails field of GetSecretValue API calls.

Hybrid post-quantum key exchange using ML-KEM for AWS Secrets Manager is available in all AWS Regions where AWS Secrets Manager is supported. To learn more, visit the AWS Secrets Manager documentation and the AWS Post-Quantum Cryptography migration page.

​AWS Secrets Manager now supports hybrid post-quantum key exchange using ML-KEM (Module-Lattice-based Key-Encapsulation Mechanism) to secure TLS connections for retrieving and managing secrets. This protection is automatically enabled in Secrets Manager Agent (version 2.0.0+), AWS Lambda Extension (version 19+), and Secrets Manager CSI Driver (version 2.0.0+). For SDK-based clients, hybrid post-quantum key exchange is available in supported AWS SDKs including Rust, Go, Node.js, Kotlin, Python (with OpenSSL 3.5+), and Java v2 (v2.35.11+).
With this launch, your applications retrieve secrets over TLS connections that combine classical key exchange with post-quantum cryptography, helping protect against both traditional cryptographic attacks and future quantum computing threats known as «harvest now, decrypt later» (HNDL). No code changes, configuration updates, or migration effort are required for customers using the latest client versions except for Java v2. For example, a microservice requiring multiple secrets at startup can now retrieve them over quantum-resistant TLS connections by simply upgrading to the latest Secrets Manager Agent version. You can verify hybrid post-quantum key exchange is active by checking CloudTrail logs for the «X25519MLKEM768» key exchange algorithm in the tlsDetails field of GetSecretValue API calls.
Hybrid post-quantum key exchange using ML-KEM for AWS Secrets Manager is available in all AWS Regions where AWS Secrets Manager is supported. To learn more, visit the AWS Secrets Manager documentation and the AWS Post-Quantum Cryptography migration page.  

Today, AWS announces increased Amazon Elastic Block Store (Amazon EBS) performance for Amazon EC2 C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes.

EC2 C8gn, M8gn, and R8gn instances are network optimized instances powered by AWS Graviton4 processors and latest 6th generation AWS Nitro Cards. With the latest enhancements to AWS Nitro System, we have doubled the maximum EBS performance on these instances in 48xlarge and metal-48xl sizes, from 60 Gbps of EBS bandwidth and 240,000 IOPS to 120 Gbps of EBS bandwidth and 480,000 IOPS. Customers running network-intensive workloads while requiring additional block storage performance such as data analytics and high-performance file systems can benefit from the improved EBS performance.

All existing and new C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes launched starting today will benefit from this performance increase at no additional cost. For running instances, customers can stop and start instances to enable this performance increase. The higher EBS performance is available in all AWS regions where these instance types are generally available today.

To learn more, see Amazon C8gn, M8gn, and R8gn Instances and EBS-optimized instance types. 

​Today, AWS announces increased Amazon Elastic Block Store (Amazon EBS) performance for Amazon EC2 C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes.
EC2 C8gn, M8gn, and R8gn instances are network optimized instances powered by AWS Graviton4 processors and latest 6th generation AWS Nitro Cards. With the latest enhancements to AWS Nitro System, we have doubled the maximum EBS performance on these instances in 48xlarge and metal-48xl sizes, from 60 Gbps of EBS bandwidth and 240,000 IOPS to 120 Gbps of EBS bandwidth and 480,000 IOPS. Customers running network-intensive workloads while requiring additional block storage performance such as data analytics and high-performance file systems can benefit from the improved EBS performance.
All existing and new C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes launched starting today will benefit from this performance increase at no additional cost. For running instances, customers can stop and start instances to enable this performance increase. The higher EBS performance is available in all AWS regions where these instance types are generally available today.
To learn more, see Amazon C8gn, M8gn, and R8gn Instances and EBS-optimized instance types.   

• Categoría: Noticias de Microsoft

abril 14, 2026

MAI-Image-2-Efficient: calidad insignia, costo 41% menor

Por: Equipo de MAI Superintelligence.

Disponible ahora en Microsoft Foundryy MAI Playground

Construimos MAI-Image-2 para que fuera nuestro mejor modelo de texto a imagen — fotorrealista, expresivo, con texto dentro de la imagen fiable.

Hoy hacemos todo eso más rápido y barato.

Conozcan MAI-Image-2-Efficient.

Calidad lista para producción. Construido para la velocidad y la escala. 22% más rápido y 4 veces más eficiente¹. Y con un precio casi un 41% más bajo — 5 dólares por cada 1 millón de tokens de entrada de texto, 19,50 dólares por cada 1 millón de tokens de salida de imagen.

Eso no es solo más rápido que nuestro propio modelo insignia. Es un 40% más rápido en promedio que otros modelos principales de texto a imagen².

Dos modelos, dos trabajos

MAI-Image-2-Efficient es su caballo de batalla de producción. Úsenlo cuando necesiten volumen, rapidez y un control estricto de costes — fotos de producto, creatividades de marketing, maquetas de interfaz, activos de marca, pipelines por lotes. Este modelo gestiona textos cortos como encabezados y etiquetas de forma limpia, y está diseñado para funcionar en flujos de trabajo interactivos en tiempo real sin despeinarse.

MAI-Image-2 es su herramienta de precisión. Úsenlo cuando el encargo requiera la mayor fidelidad: retratos, escenas fotorrealistas, looks estilizados como anime o ilustración, y texto más largo o complejo en imagen. Este es el modelo para los entregables finales donde cada detalle importa.

Empiecen a construir ahora

MAI-Image-2-Efficient está disponible hoy en Microsoft Foundry y MAI Playground³. Sin lista de espera, sin vista previa — sólo actívenlo y listo. También se va a desplegar en Copilot y Bing, con más superficies como PowerPoint más adelante.

Socios como Shutterstock ya lo han probado con resultados prometedores:

«MAI-Image-2-Efficient muestra un fuerte progreso en fidelidad rápida y usabilidad creativa a lo largo de una variedad de flujos de trabajo. En nuestro trabajo de evaluación, analizamos con detenimiento cómo los modelos traducen la intención en resultados consistentes y listos para producción, y este modelo va en la dirección correcta. Ese nivel de fiabilidad es lo que en verdad importa cuando los equipos pasan de la experimentación al uso en el mundo real.» – Vanessa Salvo, directora de producto principal, Shutterstock

Esto es solo el principio. Más modelos llegarán más adelante— estén atentos.

Descargar tarjeta de modelo

1. Tal como se probó el 13 de abril de 2026. Comparado con MAI-Image-2 cuando se normaliza por latencia y uso de GPU. Rendimiento por GPU vs MAI-Image-2 en NVIDIA H100 a 1024×1024; se midió con tamaños de lote optimizados y objetivos de latencia coincididos. Los resultados varían según el tamaño del lote, la concurrencia y las restricciones de latencia.
2. Tal como se probó el 13 de abril de 2026. Comparado con Gemini 3.1 Flash (alto razonamiento), Gemini 3.1 Flash Image y Gemini 3 Pro Image: medido a p50 de latencia mediante AI Studio API (1:1, 1K imágenes; razonamiento mínimo salvo que se indique; búsqueda web deshabilitada). MAI-Image-2, MAI-Image-2e, GPT-Image-1.5-High: Medido a una latencia p50 mediante Foundry API.
3. MAI Playground está disponible en mercados seleccionados, incluidos Estados Unidos. Más adelante en los países de la UE.

Etiquetas:

• IA
• Innovación

The post MAI-Image-2-Efficient: calidad insignia, costo 41% menor appeared first on Source LATAM.

​The post MAI-Image-2-Efficient: calidad insignia, costo 41% menor appeared first on Source LATAM.  

NVIDIA’s Nemotron-3-Super-120B, Qwen3.5-9B, and Qwen3.5-27B 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 agentic reasoning, multilingual coding, and advanced instruction following, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

These models address different enterprise AI challenges with specialized capabilities:
Nemotron-3-Super-120B is optimized for collaborative agents and high-volume workloads such as IT ticket automation. It employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture with Mamba-2 and MoE layers, enabling strong agentic, reasoning, and conversational capabilities useful for multi-agent applications like software development and cybersecurity triaging.
Qwen 3.5 9B excels in multilingual coding, instruction following, and long-horizon planning, automating software development workflows and executing complex, multi-step office tasks. Its compact design balances efficiency and performance for resource-constrained environments.
Qwen 3.5 27B provides deeper contextual understanding, extended reasoning capabilities, and enhanced spatial/complex scenario comprehension, ideal for advanced multimodal reasoning and large-scale document processing.
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 Nemotron-3-Super-120B, Qwen3.5-9B, and Qwen3.5-27B 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 agentic reasoning, multilingual coding, and advanced instruction following, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: Nemotron-3-Super-120B is optimized for collaborative agents and high-volume workloads such as IT ticket automation. It employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture with Mamba-2 and MoE layers, enabling strong agentic, reasoning, and conversational capabilities useful for multi-agent applications like software development and cybersecurity triaging. Qwen 3.5 9B excels in multilingual coding, instruction following, and long-horizon planning, automating software development workflows and executing complex, multi-step office tasks. Its compact design balances efficiency and performance for resource-constrained environments. Qwen 3.5 27B provides deeper contextual understanding, extended reasoning capabilities, and enhanced spatial/complex scenario comprehension, ideal for advanced multimodal reasoning and large-scale document processing. 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.  

Amazon Quick now supports document-level access controls (ACLs) for Google Drive knowledge bases, enabling organizations to maintain native Google Drive permissions when indexing content. Quick combines ACL replication for efficient pre-retrieval filtering with an additional layer of real-time permission checks directly with Google Drive at query time. This dual approach means you get the performance benefits of indexed ACLs while also guarding against stale or incorrectly mapped permission data. When a user submits a query, Quick verifies their current permissions with Google Drive before generating a response—ensuring answers are based on live access rights.

With document-level access controls, Amazon Quick now respects individual file and folder permissions from Google Drive. This feature is available in all AWS Regions where Amazon Quick is available.

To get started, create or update a Google Drive knowledge base in the Amazon Quick console and configure document-level access controls in your integration settings. For more information, see Google Drive integration in the Amazon Quick User Guide.

​Amazon Quick now supports document-level access controls (ACLs) for Google Drive knowledge bases, enabling organizations to maintain native Google Drive permissions when indexing content. Quick combines ACL replication for efficient pre-retrieval filtering with an additional layer of real-time permission checks directly with Google Drive at query time. This dual approach means you get the performance benefits of indexed ACLs while also guarding against stale or incorrectly mapped permission data. When a user submits a query, Quick verifies their current permissions with Google Drive before generating a response—ensuring answers are based on live access rights. With document-level access controls, Amazon Quick now respects individual file and folder permissions from Google Drive. This feature is available in all AWS Regions where Amazon Quick is available.
To get started, create or update a Google Drive knowledge base in the Amazon Quick console and configure document-level access controls in your integration settings. For more information, see Google Drive integration in the Amazon Quick User Guide.  

Amazon Redshift further optimizes the processing of top-k queries (queries with ORDER BY and LIMIT clauses) by intelligently skipping irrelevant data blocks to return results faster, dramatically reducing the amount of data processed. This optimization reorders and efficiently adjusts the data blocks to be read based on the ORDER BY column’s min/max values, maintaining only the K most qualifying rows in memory. When the ORDER BY column is sorted or partially sorted, Amazon Redshift now processes only the minimal data blocks needed rather than scanning entire tables, eliminating unnecessary I/O and compute overhead.

This enhancement particularly benefits top-k queries when the data permanently stores in descending order (ORDER BY … DESC LIMIT K) on large tables where qualifying rows are appended at the end of the data storage. Common examples include:

• Finding the k most recent orders from millions or billions of transactions
• Retrieving top-k best performing products or k worst performing products (top-k in descending order) from your sales catalog containing hundreds of thousands stock keeping units (SKUs) and millions or billions of sales transactions associated with all product SKUs in your sales catalog
• Finding the top-k most recent or top-k oldest (top k in descending order) prompts inferred by a foundational large language model (LLM) out of billions of prompts.

With this new optimization, top-k query performance improves dramatically. This optimization for top-k queries is now available in Amazon Redshift at no additional cost starting with patch release P199 across all AWS regions where Amazon Redshift is available. This optimization automatically applies to eligible queries without requiring any query rewrites or configuration changes.

​Amazon Redshift further optimizes the processing of top-k queries (queries with ORDER BY and LIMIT clauses) by intelligently skipping irrelevant data blocks to return results faster, dramatically reducing the amount of data processed. This optimization reorders and efficiently adjusts the data blocks to be read based on the ORDER BY column’s min/max values, maintaining only the K most qualifying rows in memory. When the ORDER BY column is sorted or partially sorted, Amazon Redshift now processes only the minimal data blocks needed rather than scanning entire tables, eliminating unnecessary I/O and compute overhead.
This enhancement particularly benefits top-k queries when the data permanently stores in descending order (ORDER BY … DESC LIMIT K) on large tables where qualifying rows are appended at the end of the data storage. Common examples include:

Finding the k most recent orders from millions or billions of transactions
Retrieving top-k best performing products or k worst performing products (top-k in descending order) from your sales catalog containing hundreds of thousands stock keeping units (SKUs) and millions or billions of sales transactions associated with all product SKUs in your sales catalog
Finding the top-k most recent or top-k oldest (top k in descending order) prompts inferred by a foundational large language model (LLM) out of billions of prompts.

With this new optimization, top-k query performance improves dramatically. This optimization for top-k queries is now available in Amazon Redshift at no additional cost starting with patch release P199 across all AWS regions where Amazon Redshift is available. This optimization automatically applies to eligible queries without requiring any query rewrites or configuration changes.  

Amazon CloudWatch Logs Insights saved queries now support parameters, allowing you to pass values to reusable query templates with placeholders. This eliminates the need to maintain multiple copies of nearly identical queries that differ only in specific values such as log levels, service names, or time intervals.

You can define up to 20 parameters in a query, with each parameter supporting optional default values. For example, you can create a single template to query logs by severity level (such as ERROR or WARN) and pass different service names each time you run it. To execute a query with parameters, invoke it using the query name prefixed with $ and pass your parameter values, such as $ErrorsByService(logLevel=»ERROR», serviceName=»OrderEntry»). You can also use multiple saved queries with parameters together for complex log analysis, significantly reducing query maintenance overhead while improving reusability.

Saved queries with parameters are available in all commercial AWS regions. You can create and use saved queries with parameters using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.

​Amazon CloudWatch Logs Insights saved queries now support parameters, allowing you to pass values to reusable query templates with placeholders. This eliminates the need to maintain multiple copies of nearly identical queries that differ only in specific values such as log levels, service names, or time intervals. You can define up to 20 parameters in a query, with each parameter supporting optional default values. For example, you can create a single template to query logs by severity level (such as ERROR or WARN) and pass different service names each time you run it. To execute a query with parameters, invoke it using the query name prefixed with $ and pass your parameter values, such as $ErrorsByService(logLevel=»ERROR», serviceName=»OrderEntry»). You can also use multiple saved queries with parameters together for complex log analysis, significantly reducing query maintenance overhead while improving reusability. Saved queries with parameters are available in all commercial AWS regions. You can create and use saved queries with parameters using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.  

Amazon OpenSearch Serverless introduces support for Derived Source, a new feature that can help reduce the amount of storage required for your OpenSearch Service collections. With derived source support, you can skip storing source fields and dynamically derive them when required. 

With Derived Source, OpenSearch Serverless reconstructs the _source field on the fly using the values already stored in the index, eliminating the need to maintain a separate copy of the original document. This can significantly reduce storage consumption, particularly for time-series and log analytics collections where documents contain many indexed fields. You can enable derived source at the index level when creating or updating index mappings.

Derived Source support is available today in all AWS Regions where Amazon OpenSearch Serverless is supported. For more information, see the Amazon OpenSearch Serverless documentation.

​Amazon OpenSearch Serverless introduces support for Derived Source, a new feature that can help reduce the amount of storage required for your OpenSearch Service collections. With derived source support, you can skip storing source fields and dynamically derive them when required. 
With Derived Source, OpenSearch Serverless reconstructs the _source field on the fly using the values already stored in the index, eliminating the need to maintain a separate copy of the original document. This can significantly reduce storage consumption, particularly for time-series and log analytics collections where documents contain many indexed fields. You can enable derived source at the index level when creating or updating index mappings.
Derived Source support is available today in all AWS Regions where Amazon OpenSearch Serverless is supported. For more information, see the Amazon OpenSearch Serverless documentation.  

Today we are announcing the release of the Aurora DSQL Connector for PHP (PDO_PGSQL) that makes it easy to build PHP applications on Aurora DSQL. The PHP Connector streamlines authentication and eliminates security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing PDO_PGSQL features.

The connector handles IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. It also provides opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, making it easier to develop client retry logic and manage AWS credentials.

To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our GitHub page for the PHP connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.    

​Today we are announcing the release of the Aurora DSQL Connector for PHP (PDO_PGSQL) that makes it easy to build PHP applications on Aurora DSQL. The PHP Connector streamlines authentication and eliminates security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing PDO_PGSQL features. The connector handles IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. It also provides opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, making it easier to develop client retry logic and manage AWS credentials. To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our GitHub page for the PHP connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.