agosto 11, 2026

• Categoría: Noticias de Microsoft

Ivonne Mejía, nueva directora general de Microsoft México

• Source LATAM

Ciudad de México – Microsoft anuncia el nombramiento de Ivonne Mejía como nueva directora general de Microsoft México a partir del 1 de septiembre de 2026. En esta posición, liderará la estrategia para crear valor para los clientes, acelerar el crecimiento de Microsoft en México y fortalecer las alianzas con clientes y socios de negocio. Durante el mes de agosto, Ivonne colaborará estrechamente con Rafael Sánchez, quien estuvo al frente de Microsoft México desde 2022, para asegurar una transición ordenada y fluida en la dirección general de la compañía en el país.

Con más de 15 años de trayectoria en Microsoft, Ivonne aporta una sólida experiencia y un profundo conocimiento del negocio, construidos a través de posiciones de liderazgo en áreas estratégicas de la compañía. A lo largo de su carrera, ha encabezado iniciativas de transformación digital e innovación, acelerado resultados de negocio y consolidado relaciones de confianza con clientes y socios, combinando visión estratégica, capacidad de ejecución y un firme compromiso con el crecimiento del ecosistema tecnológico.

“Estoy profundamente agradecida por esta oportunidad y por la confianza. Creo en el potencial de nuestro país y en la gente que lo hace posible. Asumo este rol con una convicción clara: nuestro crecimiento vendrá de nuestra gente y de nuestros clientes y socios de negocio. Mi enfoque será construir juntos, pensar en grande y aprovechar al máximo la fuerza de todo Microsoft para servir mejor a México”.

El liderazgo de Ivonne se distingue por su capacidad para unir perspectivas, potenciar el talento y transformar equipos diversos en equipos de alto desempeño. Esta combinación de visión estratégica, cercanía y enfoque en las personas será fundamental para conducir a Microsoft México hacia su siguiente capítulo, acelerar el crecimiento de la compañía y ampliar el valor que genera para sus clientes, socios de negocio y el ecosistema tecnológico del país.

La trayectoria de Ivonne combina una sólida formación académica con una valiosa experiencia en los ámbitos tecnológico y comercial. Es ingeniera electrónica por la Universidad Nacional de Colombia, cuenta con un MBA del Tecnológico de Monterrey y cursó un Diplomado en Administración y Dirección de Empresas en el IPADE. Antes de incorporarse a Microsoft en 2010, desarrolló su carrera en Rockwell Automation, donde adquirió una visión integral del negocio y fortaleció su experiencia comercial, sentando las bases de un liderazgo que conecta tecnología, estrategia y cercanía con los clientes.

El legado de Rafael Sánchez: transformación, crecimiento e innovación en México

Después de más de cuatro años al frente de Microsoft México, Rafa Sánchez ha decidido cerrar este capítulo de su trayectoria para emprender un nuevo proyecto profesional fuera de Microsoft. Durante su gestión como director general, impulsó el crecimiento de la organización en el país y fortaleció su presencia en la industria tecnológica, dejando un legado significativo para la compañía, sus clientes, socios de negocio y el ecosistema digital en México. Así mismo, impulsó una cultura basada en la colaboración, la inclusión y la excelencia, y lideró iniciativas clave para la organización.

Bajo su liderazgo se consolidó la transformación de las operaciones en el país, se fortalecieron las capacidades comerciales, se inauguró la Región de Centros de Datos de Querétaro y se relanzó el Microsoft Innovation Hub. También promovió iniciativas de diversidad, desarrollo de talento y representación de la industria, contribuyendo a posicionar a México como un referente dentro de Microsoft y a preparar al país para aprovechar las oportunidades de la era de la IA.

Con más de 30 años de experiencia en la industria de tecnologías de la información, Rafael iniciará una nueva etapa en su trayectoria profesional dentro de la industria de TI, que se dará a conocer próximamente.

El nombramiento de Ivonne Mejía marca un nuevo capítulo para Microsoft México y un hito en la historia de la compañía en el país: la primera mujer en asumir la dirección general, desde donde liderará esta nueva etapa de crecimiento, innovación e impacto.

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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.

Contacto de prensa:   

Microsoft                                            Assembly México    

Tere Rodríguez                     microsoftMexico@assemblyinc.com     

teresar@microsoft.com        55 5350 1500    

Etiquetas:

• Anuncios
• México
• Press Releases

The post Ivonne Mejía, nueva directora general de Microsoft México appeared first on Source LATAM.

​The post Ivonne Mejía, nueva directora general de Microsoft México appeared first on Source LATAM.  

NVIDIA’s LocateAnything-3B, Qwen’s Qwen-AgentWorld-35B-A3B, and Qwen’s Qwen3.5-122B-A10B 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 visual grounding, agent environment simulation, and large-scale multimodal reasoning, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

These models address different enterprise AI challenges with specialized capabilities:

LocateAnything-3B is optimized for fast, high-quality visual grounding and object localization from natural language instructions. It uses a Parallel Box Decoding (PBD) framework that decodes bounding boxes and points as atomic units in a single step, preserving geometric coherence and unlocking substantial parallelism. It enables precise object localization, dense detection, and point-based localization across diverse domains in both Enterprise Intelligence and Physical AI applications.

Qwen-AgentWorld-35B-A3B excels in simulating agent environments across seven interaction domains: tool calling, search, terminal, software engineering, Android, web, and OS interaction. It is the first language world model to cover all seven domains within a single model, predicting next environment states given an agent’s action and interaction history via long chain-of-thought reasoning—trained on over 10 million real-world interaction trajectories.

Qwen3.5-122B-A10B provides high-performance multimodal reasoning with production-friendly efficiency. It features 122B total parameters with only 10B activated per token through a hybrid architecture integrating Gated Delta Networks with sparse Mixture-of-Experts (256 experts), delivering strong reasoning, coding, agents, and visual understanding performance with a native 262K context window and minimal latency overhead.

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 LocateAnything-3B, Qwen’s Qwen-AgentWorld-35B-A3B, and Qwen’s Qwen3.5-122B-A10B 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 visual grounding, agent environment simulation, and large-scale multimodal reasoning, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
LocateAnything-3B is optimized for fast, high-quality visual grounding and object localization from natural language instructions. It uses a Parallel Box Decoding (PBD) framework that decodes bounding boxes and points as atomic units in a single step, preserving geometric coherence and unlocking substantial parallelism. It enables precise object localization, dense detection, and point-based localization across diverse domains in both Enterprise Intelligence and Physical AI applications.
Qwen-AgentWorld-35B-A3B excels in simulating agent environments across seven interaction domains: tool calling, search, terminal, software engineering, Android, web, and OS interaction. It is the first language world model to cover all seven domains within a single model, predicting next environment states given an agent’s action and interaction history via long chain-of-thought reasoning—trained on over 10 million real-world interaction trajectories.
Qwen3.5-122B-A10B provides high-performance multimodal reasoning with production-friendly efficiency. It features 122B total parameters with only 10B activated per token through a hybrid architecture integrating Gated Delta Networks with sparse Mixture-of-Experts (256 experts), delivering strong reasoning, coding, agents, and visual understanding performance with a native 262K context window and minimal latency overhead.
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 3.5 Lightning is now available on Amazon SageMaker JumpStart, giving AWS customers access to the fastest open model in its class for persistent agent workloads and rapid task execution.

Nemotron 3.5 Lightning is engineered for persistent agents and high-throughput enterprise automation across domains including personal assistants, financial document processing, cybersecurity triage, and telecom operations. Built on a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters and just 3B active per forward pass, it achieves up to 4x the throughput (~410 tokens/sec) and 30% faster task completion over comparable models. Distilled from Nemotron 3 Ultra, it handles up to 1M tokens of context via DFlash speculative decoding and integrates directly with popular agent harnesses. The model is fully open-trained on open datasets thereby allowing enterprises to post-train for their own tools, workflows, and policies, and deploy with complete ownership across edge, on-premises, or cloud infrastructure.

With SageMaker JumpStart, customers can deploy this model in a few clicks to power their specific AI workloads.

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 3.5 Lightning is now available on Amazon SageMaker JumpStart, giving AWS customers access to the fastest open model in its class for persistent agent workloads and rapid task execution.
Nemotron 3.5 Lightning is engineered for persistent agents and high-throughput enterprise automation across domains including personal assistants, financial document processing, cybersecurity triage, and telecom operations. Built on a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters and just 3B active per forward pass, it achieves up to 4x the throughput (~410 tokens/sec) and 30% faster task completion over comparable models. Distilled from Nemotron 3 Ultra, it handles up to 1M tokens of context via DFlash speculative decoding and integrates directly with popular agent harnesses. The model is fully open-trained on open datasets thereby allowing enterprises to post-train for their own tools, workflows, and policies, and deploy with complete ownership across edge, on-premises, or cloud infrastructure.
With SageMaker JumpStart, customers can deploy this model in a few clicks to power their specific AI workloads.
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 Connect Customer now provides a performance dashboard for cases that helps managers monitor case volume, resolution trends, and performance against service level agreement (SLA) targets. Managers can compare current and prior-period performance across metrics such as cases created, average resolution time, first-contact resolution percentage, and SLA achievement rate. They can also analyze trends across dimensions such as case template, assigned user, or assigned queue. For example, a manager can identify that the billing team missed more SLA targets for refund cases than in the prior period, investigate the causes, and prioritize process improvements.

Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Cases webpage and documentation.

​Amazon Connect Customer now provides a performance dashboard for cases that helps managers monitor case volume, resolution trends, and performance against service level agreement (SLA) targets. Managers can compare current and prior-period performance across metrics such as cases created, average resolution time, first-contact resolution percentage, and SLA achievement rate. They can also analyze trends across dimensions such as case template, assigned user, or assigned queue. For example, a manager can identify that the billing team missed more SLA targets for refund cases than in the prior period, investigate the causes, and prioritize process improvements.
Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Cases webpage and documentation.  

Redis’s langcache-embed-v3-small, JetBrains’ Mellum2-12B-A2.5B-Thinking, and LightOn’s LightOnOCR-2-1B 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 semantic caching optimization, code-focused reasoning, and end-to-end document OCR, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

langcache-embed-v3-small is optimized for semantic caching in LLM applications. It maps sentences and paragraphs into a dense vector space purpose-built for identifying semantically equivalent queries regardless of phrasing, enabling intelligent cache hits that reduce redundant LLM calls and accelerate response times in high-volume inference workloads.

Mellum2-12B-A2.5B-Thinking excels in code generation, debugging, multi-step reasoning, and agentic coding workflows. It uses a Mixture-of-Experts architecture (64 experts, 8 activated per token), activating only 2.5B of its 12B total parameters per forward pass with a 131,072-token context length. It emits explicit chain-of-thought reasoning traces before final answers, delivering high-throughput, low-latency inference ideal for routing, RAG, sub-agents, and private deployments.

LightOnOCR-2-1B provides end-to-end multilingual document-to-text conversion for PDFs, scans, and images without brittle OCR pipelines. This 1B-parameter vision-language model directly transduces page images into clean, naturally ordered text, achieving state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.

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.

​Redis’s langcache-embed-v3-small, JetBrains’ Mellum2-12B-A2.5B-Thinking, and LightOn’s LightOnOCR-2-1B 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 semantic caching optimization, code-focused reasoning, and end-to-end document OCR, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
langcache-embed-v3-small is optimized for semantic caching in LLM applications. It maps sentences and paragraphs into a dense vector space purpose-built for identifying semantically equivalent queries regardless of phrasing, enabling intelligent cache hits that reduce redundant LLM calls and accelerate response times in high-volume inference workloads.
Mellum2-12B-A2.5B-Thinking excels in code generation, debugging, multi-step reasoning, and agentic coding workflows. It uses a Mixture-of-Experts architecture (64 experts, 8 activated per token), activating only 2.5B of its 12B total parameters per forward pass with a 131,072-token context length. It emits explicit chain-of-thought reasoning traces before final answers, delivering high-throughput, low-latency inference ideal for routing, RAG, sub-agents, and private deployments.
LightOnOCR-2-1B provides end-to-end multilingual document-to-text conversion for PDFs, scans, and images without brittle OCR pipelines. This 1B-parameter vision-language model directly transduces page images into clean, naturally ordered text, achieving state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.
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.  

Z.ai’s GLM-5.2 FP8, NVIDIA’s Nemotron-Nano-12B-v2, and Z.ai’s GLM-OCR 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 long-horizon agentic engineering, efficient hybrid reasoning, and advanced document understanding, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

GLM-5.2 FP8 is optimized for long-horizon tasks and agentic engineering workflows such as full-cycle software development from requirements to deployment. It delivers a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, provides a truly usable 1M-token context window, enabling it to handle project-level engineering context, execute long-running tasks reliably, follow engineering standards consistently, and complete full development workflows in a single task.

NVIDIA-Nemotron-Nano-12B-v2 excels in unified reasoning and non-reasoning tasks with high inference throughput, making it ideal for enterprise applications requiring both accuracy and efficiency. It uses a hybrid Mamba-2 and Transformer architecture with a 128K context length, generating reasoning traces before concluding with final responses. Its compact 12B parameter design achieves comparable or better accuracy than leading open models while delivering up to 6x higher inference throughput.

GLM-OCR provides accurate, fast, and comprehensive document understanding for complex real-world materials including scanned PDFs, handwritten notes, dense academic papers with formulas, multi-column tables, code documentation, and multilingual text. This 0.9B-parameter multimodal model reconstructs structure, tables, and formulas into clean Markdown, JSON, or LaTeX, with latency low enough for real-time services and edge devices—ideal for large-scale document processing and invoice extraction workflows.

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.

​Z.ai’s GLM-5.2 FP8, NVIDIA’s Nemotron-Nano-12B-v2, and Z.ai’s GLM-OCR 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 long-horizon agentic engineering, efficient hybrid reasoning, and advanced document understanding, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
GLM-5.2 FP8 is optimized for long-horizon tasks and agentic engineering workflows such as full-cycle software development from requirements to deployment. It delivers a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, provides a truly usable 1M-token context window, enabling it to handle project-level engineering context, execute long-running tasks reliably, follow engineering standards consistently, and complete full development workflows in a single task.
NVIDIA-Nemotron-Nano-12B-v2 excels in unified reasoning and non-reasoning tasks with high inference throughput, making it ideal for enterprise applications requiring both accuracy and efficiency. It uses a hybrid Mamba-2 and Transformer architecture with a 128K context length, generating reasoning traces before concluding with final responses. Its compact 12B parameter design achieves comparable or better accuracy than leading open models while delivering up to 6x higher inference throughput.
GLM-OCR provides accurate, fast, and comprehensive document understanding for complex real-world materials including scanned PDFs, handwritten notes, dense academic papers with formulas, multi-column tables, code documentation, and multilingual text. This 0.9B-parameter multimodal model reconstructs structure, tables, and formulas into clean Markdown, JSON, or LaTeX, with latency low enough for real-time services and edge devices—ideal for large-scale document processing and invoice extraction workflows.
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.  

Black Forest Labs’ FLUX.2-small-decoder and Google’s gemma-4-12B-it 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 efficient image generation decoding and unified multimodal understanding, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

FLUX.2-small-decoder is optimized for faster image decoding with lower VRAM usage in FLUX.2 image generation pipelines. It is a distilled VAE decoder that serves as a drop-in replacement for the standard FLUX.2 decoder, delivering approximately 1.4× faster decoding speed at 1.4× lower VRAM consumption with minimal to zero quality loss. Benefits increase at higher resolutions where the decoder processes more pixels, making it ideal for production-grade image generation workloads at scale.

gemma-4-12B-it excels in unified multimodal understanding across text, image, and audio inputs with native support for function calling and agentic workflows. It features an encoder-free architecture where all modalities flow directly into a single decoder-only transformer, delivering performance nearing Google’s larger 26B MoE model at less than half the memory footprint. Compact enough to run on 16GB of RAM, it enables powerful multimodal and agentic experiences for enterprise 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 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.

​Black Forest Labs’ FLUX.2-small-decoder and Google’s gemma-4-12B-it 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 efficient image generation decoding and unified multimodal understanding, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
FLUX.2-small-decoder is optimized for faster image decoding with lower VRAM usage in FLUX.2 image generation pipelines. It is a distilled VAE decoder that serves as a drop-in replacement for the standard FLUX.2 decoder, delivering approximately 1.4× faster decoding speed at 1.4× lower VRAM consumption with minimal to zero quality loss. Benefits increase at higher resolutions where the decoder processes more pixels, making it ideal for production-grade image generation workloads at scale.
gemma-4-12B-it excels in unified multimodal understanding across text, image, and audio inputs with native support for function calling and agentic workflows. It features an encoder-free architecture where all modalities flow directly into a single decoder-only transformer, delivering performance nearing Google’s larger 26B MoE model at less than half the memory footprint. Compact enough to run on 16GB of RAM, it enables powerful multimodal and agentic experiences for enterprise 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 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 EC2 High Memory U7in-24TB instances (u7in-24tb.224xlarge) are now available in AWS South America (São Paulo) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page.

​Amazon EC2 High Memory U7in-24TB instances (u7in-24tb.224xlarge) are now available in AWS South America (São Paulo) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.  

Amazon GameLift Streams now manages shader cache capture and distribution for your applications. You capture a shader cache from a stream session, and the service automatically makes it available for future sessions across your streaming locations. No application changes are required.

Capturing shader caches can help reduce loading times and visual stuttering, during the session. With service-managed shader caching, you designate a stream session for capture and run your application to generate the cache. Amazon GameLift Streams then replicates the cache to compatible stream groups and locations, and loads it automatically in future sessions.

You can monitor shader cache status and storage size using the ListApplicationShaderCaches API or the Amazon GameLift Streams console. The feature supports Linux (Ubuntu 22.04), Proton, and Windows Server 2022 runtimes.

You are charged for storage of the latest version of each shader cache. For pricing details, visit the Amazon GameLift Streams pricing page. For supported Regions, see the AWS Region table.

​Amazon GameLift Streams now manages shader cache capture and distribution for your applications. You capture a shader cache from a stream session, and the service automatically makes it available for future sessions across your streaming locations. No application changes are required. Capturing shader caches can help reduce loading times and visual stuttering, during the session. With service-managed shader caching, you designate a stream session for capture and run your application to generate the cache. Amazon GameLift Streams then replicates the cache to compatible stream groups and locations, and loads it automatically in future sessions. You can monitor shader cache status and storage size using the ListApplicationShaderCaches API or the Amazon GameLift Streams console. The feature supports Linux (Ubuntu 22.04), Proton, and Windows Server 2022 runtimes. You are charged for storage of the latest version of each shader cache. For pricing details, visit the Amazon GameLift Streams pricing page. For supported Regions, see the AWS Region table.  

Amazon EC2 introduces application status checks, a new status check that helps customers detect and respond to application-level issues on their EC2 instances. With application status checks, EC2 monitors applications to detect issues such as a web server that has stopped accepting requests, a Docker daemon that is not running, an incorrect networking configuration, or a network interface that is no longer passing traffic.

Customers rely on EC2 status checks today to receive alerts when an instance or the underlying system is unreachable. However, to monitor application issues, customers had to build and maintain their own monitoring solution. Now, with application status checks customers can monitor the status of their applications running on EC2 instances alongside existing EC2 instance and system status checks. Customers create a check by specifying the protocol, port, and path to monitor, along with the response codes that indicate a healthy application. After customers associate the check with their instances by instance ID or tag, Amazon EC2 sends HTTP or HTTPS requests to that port and path and reports on the application’s status every 60 seconds. Auto Scaling groups act on application status, initiating recovery by replacing instances when their applications report unhealthy.

Application status checks are available in all commercial AWS Regions and AWS GovCloud (US) Regions.

To get started with application status checks and review pricing, see the Amazon EC2 User Guide.

​Amazon EC2 introduces application status checks, a new status check that helps customers detect and respond to application-level issues on their EC2 instances. With application status checks, EC2 monitors applications to detect issues such as a web server that has stopped accepting requests, a Docker daemon that is not running, an incorrect networking configuration, or a network interface that is no longer passing traffic.
Customers rely on EC2 status checks today to receive alerts when an instance or the underlying system is unreachable. However, to monitor application issues, customers had to build and maintain their own monitoring solution. Now, with application status checks customers can monitor the status of their applications running on EC2 instances alongside existing EC2 instance and system status checks. Customers create a check by specifying the protocol, port, and path to monitor, along with the response codes that indicate a healthy application. After customers associate the check with their instances by instance ID or tag, Amazon EC2 sends HTTP or HTTPS requests to that port and path and reports on the application’s status every 60 seconds. Auto Scaling groups act on application status, initiating recovery by replacing instances when their applications report unhealthy.
Application status checks are available in all commercial AWS Regions and AWS GovCloud (US) Regions.
To get started with application status checks and review pricing, see the Amazon EC2 User Guide.