AWS Config added support for a service-linked recorder, a new type of AWS Config recorder that is managed by an AWS service and can record configuration data on service-specific resources, such as the new Amazon CloudWatch telemetry configurations audit. By enabling the service-linked recorder in Amazon CloudWatch, you gain centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces.

With service-linked recorders, an AWS service can deploy and manage an AWS Config recorder on your behalf to discover resources and utilize the configuration data to provide differentiated features. For example, an Amazon CloudWatch managed service-linked recorder helps you identify monitoring gaps within specific critical resources within your organization, providing a centralized, single-pane view of telemetry configuration status. Service-linked recorders are immutable to ensure consistency, prevention of configuration drift, and simplified experience. Service-linked recorders operate independently of any existing AWS Config recorder, if one is enabled. This allows you to independently manage your AWS Config recorder for your specific use cases while authorized AWS services can manage the service-linked recorder for feature specific requirements.

Amazon CloudWatch managed service-linked recorder is now available in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney) Europe (Frankfurt), Europe (Ireland), Europe (Stockholm) regions. The AWS Config service-linked recorder specific to Amazon CloudWatch telemetry configuration feature is available to customers at no additional cost.

To learn more, please refer to our documentation.
 

​AWS Config added support for a service-linked recorder, a new type of AWS Config recorder that is managed by an AWS service and can record configuration data on service-specific resources, such as the new Amazon CloudWatch telemetry configurations audit. By enabling the service-linked recorder in Amazon CloudWatch, you gain centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. With service-linked recorders, an AWS service can deploy and manage an AWS Config recorder on your behalf to discover resources and utilize the configuration data to provide differentiated features. For example, an Amazon CloudWatch managed service-linked recorder helps you identify monitoring gaps within specific critical resources within your organization, providing a centralized, single-pane view of telemetry configuration status. Service-linked recorders are immutable to ensure consistency, prevention of configuration drift, and simplified experience. Service-linked recorders operate independently of any existing AWS Config recorder, if one is enabled. This allows you to independently manage your AWS Config recorder for your specific use cases while authorized AWS services can manage the service-linked recorder for feature specific requirements. Amazon CloudWatch managed service-linked recorder is now available in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney) Europe (Frankfurt), Europe (Ireland), Europe (Stockholm) regions. The AWS Config service-linked recorder specific to Amazon CloudWatch telemetry configuration feature is available to customers at no additional cost. To learn more, please refer to our documentation.    

Amazon RDS (Relational Database Service) Performance Insights expands the availability of its on-demand analysis experience to 15 new regions. This feature is available for Aurora MySQL, Aurora PostgreSQL, and RDS for PostgreSQL engines.

This on-demand analysis experience, which was previously available in only 15 regions, is now available in all commercial regions. This feature allows you to analyze Performance Insights data for a time period of your choice. You can learn how the selected time period differs from normal, what went wrong, and get advice on corrective actions. Through simple-to-understand graphs and explanations, you can identify the chief contributors to performance issues. You will also get the guidance on the next steps to act on these issues. This can reduce the mean-time-to-diagnosis for database performance issues from hours to minutes.

Amazon RDS Performance Insights is a database performance tuning and monitoring feature of RDS that allows you to visually assess the load on your database and determine when and where to take action. With one click in the Amazon RDS Management Console, you can add a fully-managed performance monitoring solution to your Amazon RDS database.

To learn more about RDS Performance Insights, read the Amazon RDS User Guide and visit Performance Insights pricing for pricing details and region availability.
 

​Amazon RDS (Relational Database Service) Performance Insights expands the availability of its on-demand analysis experience to 15 new regions. This feature is available for Aurora MySQL, Aurora PostgreSQL, and RDS for PostgreSQL engines. This on-demand analysis experience, which was previously available in only 15 regions, is now available in all commercial regions. This feature allows you to analyze Performance Insights data for a time period of your choice. You can learn how the selected time period differs from normal, what went wrong, and get advice on corrective actions. Through simple-to-understand graphs and explanations, you can identify the chief contributors to performance issues. You will also get the guidance on the next steps to act on these issues. This can reduce the mean-time-to-diagnosis for database performance issues from hours to minutes. Amazon RDS Performance Insights is a database performance tuning and monitoring feature of RDS that allows you to visually assess the load on your database and determine when and where to take action. With one click in the Amazon RDS Management Console, you can add a fully-managed performance monitoring solution to your Amazon RDS database. To learn more about RDS Performance Insights, read the Amazon RDS User Guide and visit Performance Insights pricing for pricing details and region availability.    

Today, we are introducing the new ModelTrainer class and enhancing the ModelBuilder class in the SageMaker Python SDK. These updates streamline training workflows and simplify inference deployments.

The ModelTrainer class enables customers to easily set up and customize distributed training strategies on Amazon SageMaker. This new feature accelerates model training times, optimizes resource utilization, and reduces costs through efficient parallel processing. Customers can smoothly transition their custom entry points and containers from a local environment to SageMaker, eliminating the need to manage infrastructure. ModelTrainer simplifies configuration by reducing parameters to just a few core variables and providing user-friendly classes for intuitive SageMaker service interactions. Additionally, with the enhanced ModelBuilder class, customers can now easily deploy HuggingFace models, switch between developing in local environment to SageMaker, and customize their inference using their pre- and post-processing scripts. Importantly, customers can now pass the trained model artifacts from ModelTrainer class easily to ModelBuilder class, enabling a seamlessly transition from training to inference on SageMaker.

You can learn more about ModelTrainer class here, ModelBuilder enhancements here, and get started using ModelTrainer and ModelBuilder sample notebooks.

​Today, we are introducing the new ModelTrainer class and enhancing the ModelBuilder class in the SageMaker Python SDK. These updates streamline training workflows and simplify inference deployments. The ModelTrainer class enables customers to easily set up and customize distributed training strategies on Amazon SageMaker. This new feature accelerates model training times, optimizes resource utilization, and reduces costs through efficient parallel processing. Customers can smoothly transition their custom entry points and containers from a local environment to SageMaker, eliminating the need to manage infrastructure. ModelTrainer simplifies configuration by reducing parameters to just a few core variables and providing user-friendly classes for intuitive SageMaker service interactions. Additionally, with the enhanced ModelBuilder class, customers can now easily deploy HuggingFace models, switch between developing in local environment to SageMaker, and customize their inference using their pre- and post-processing scripts. Importantly, customers can now pass the trained model artifacts from ModelTrainer class easily to ModelBuilder class, enabling a seamlessly transition from training to inference on SageMaker. You can learn more about ModelTrainer class here, ModelBuilder enhancements here, and get started using ModelTrainer and ModelBuilder sample notebooks.  

We are excited to announce two new capabilities in SageMaker Inference that significantly enhance the deployment and scaling of generative AI models: Container Caching and Fast Model Loader. These innovations address critical challenges in scaling large language models (LLMs) efficiently, enabling faster response times to traffic spikes and more cost-effective scaling. By reducing model loading times and accelerating autoscaling, these features allow customers to improve the responsiveness of their generative AI applications as demand fluctuates, particularly benefiting services with dynamic traffic patterns.

Container Caching dramatically reduces the time required to scale generative AI models for inference by pre-caching container images. This eliminates the need to download them when scaling up, resulting in significant reduction in scaling time for generative AI model endpoints. Fast Model Loader streams model weights directly from Amazon S3 to the accelerator, loading models much faster compared to traditional methods. These capabilities allow customers to create more responsive auto-scaling policies, enabling SageMaker to add new instances or model copies quickly when defined thresholds are reached, thus maintaining optimal performance during traffic spikes while at the same time managing costs effectively.

These new capabilities are accessible in all AWS regions where Amazon SageMaker Inference is available. To learn more see our documentation for detailed implementation guidance.
 

​We are excited to announce two new capabilities in SageMaker Inference that significantly enhance the deployment and scaling of generative AI models: Container Caching and Fast Model Loader. These innovations address critical challenges in scaling large language models (LLMs) efficiently, enabling faster response times to traffic spikes and more cost-effective scaling. By reducing model loading times and accelerating autoscaling, these features allow customers to improve the responsiveness of their generative AI applications as demand fluctuates, particularly benefiting services with dynamic traffic patterns. Container Caching dramatically reduces the time required to scale generative AI models for inference by pre-caching container images. This eliminates the need to download them when scaling up, resulting in significant reduction in scaling time for generative AI model endpoints. Fast Model Loader streams model weights directly from Amazon S3 to the accelerator, loading models much faster compared to traditional methods. These capabilities allow customers to create more responsive auto-scaling policies, enabling SageMaker to add new instances or model copies quickly when defined thresholds are reached, thus maintaining optimal performance during traffic spikes while at the same time managing costs effectively. These new capabilities are accessible in all AWS regions where Amazon SageMaker Inference is available. To learn more see our documentation for detailed implementation guidance.    

Digital sovereignty has been a priority for AWS since its inception. AWS remains committed to offering customers the most advanced sovereignty controls and features in the cloud. With the increasing importance of digital sovereignty for public sector organizations and regulated industries, AWS is excited to announce the launch of the AWS Digital Sovereignty Competency.

The AWS Digital Sovereignty Competency curates and validates a community of AWS Partners with advanced sovereignty capabilities and solutions, including deep experience in helping customers address sovereignty and compliance requirements. These partners can assist customers with residency control, access control, resilience, survivability, and self-sufficiency.

Through this competency, customers can search for and engage with trusted local and global AWS Partners that have technically validated experience in addressing customers’ sovereignty requirements. Many partners have built sovereign solutions that leverage AWS innovations and built-in controls and security features.

In addition to these offerings, AWS Digital Sovereignty Partners provide skills and knowledge of local compliance requirements and regulations, making it easier for customers to meet their digital sovereignty requirements while benefiting from the performance, agility, security, and scale of the AWS Cloud.

​Digital sovereignty has been a priority for AWS since its inception. AWS remains committed to offering customers the most advanced sovereignty controls and features in the cloud. With the increasing importance of digital sovereignty for public sector organizations and regulated industries, AWS is excited to announce the launch of the AWS Digital Sovereignty Competency. The AWS Digital Sovereignty Competency curates and validates a community of AWS Partners with advanced sovereignty capabilities and solutions, including deep experience in helping customers address sovereignty and compliance requirements. These partners can assist customers with residency control, access control, resilience, survivability, and self-sufficiency. Through this competency, customers can search for and engage with trusted local and global AWS Partners that have technically validated experience in addressing customers’ sovereignty requirements. Many partners have built sovereign solutions that leverage AWS innovations and built-in controls and security features. In addition to these offerings, AWS Digital Sovereignty Partners provide skills and knowledge of local compliance requirements and regulations, making it easier for customers to meet their digital sovereignty requirements while benefiting from the performance, agility, security, and scale of the AWS Cloud.  

Amazon Kendra is an AI-powered search service enabling organizations to build intelligent search experiences and retrieval augmented generation (RAG) systems to power generative AI applications. Starting today, AWS customers can use a new index – the GenAI Index for RAG and intelligent search. With the Kendra GenAI Index, customers get high out-of-the-box search accuracy powered by the latest information retrieval technologies and semantic models.

Kendra GenAI Index supports mobility across AWS generative AI services like Amazon Bedrock Knowledge Base and Amazon Q Business, giving customers the flexibility to use their indexed content across different use cases. It is available as a managed retriever in Bedrock Knowledge Bases, enabling customers to create a Knowledge Base powered by the Kendra GenAI Index. Customers can also integrate such Knowledge Bases with other Bedrock Services like Guardrails, Prompt Flows, and Agents to build advanced generative AI applications. The GenAI Index supports connectors for 43 different data sources, enabling customers to easily ingest content from a variety of sources.

Kendra GenAI Index is available in the US East (N. Virginia) and US West (Oregon) regions.

To learn more, see Kendra GenAI Index in the Amazon Kendra Developer Guide. For pricing, please refer to Kendra pricing page.

​Amazon Kendra is an AI-powered search service enabling organizations to build intelligent search experiences and retrieval augmented generation (RAG) systems to power generative AI applications. Starting today, AWS customers can use a new index – the GenAI Index for RAG and intelligent search. With the Kendra GenAI Index, customers get high out-of-the-box search accuracy powered by the latest information retrieval technologies and semantic models. Kendra GenAI Index supports mobility across AWS generative AI services like Amazon Bedrock Knowledge Base and Amazon Q Business, giving customers the flexibility to use their indexed content across different use cases. It is available as a managed retriever in Bedrock Knowledge Bases, enabling customers to create a Knowledge Base powered by the Kendra GenAI Index. Customers can also integrate such Knowledge Bases with other Bedrock Services like Guardrails, Prompt Flows, and Agents to build advanced generative AI applications. The GenAI Index supports connectors for 43 different data sources, enabling customers to easily ingest content from a variety of sources. Kendra GenAI Index is available in the US East (N. Virginia) and US West (Oregon) regions. To learn more, see Kendra GenAI Index in the Amazon Kendra Developer Guide. For pricing, please refer to Kendra pricing page.  

En el dinámico panorama tecnológico actual, el dicho «la herramienta adecuada para cada trabajo» ha adquirido un nuevo significado. Aunque en el pasado las opciones de herramientas eran limitadas, hoy estamos inundados de soluciones, especialmente en el ámbito de la seguridad cibernética para los proveedores de servicios gestionados (MSP). Esta abundancia puede ser tanto una bendición como un desafío, ya que no todas las herramientas son iguales.

La importancia de elegir la herramienta adecuada

En un entorno de amenazas en constante evolución, es crucial estar siempre un paso por delante. Las amenazas avanzadas, planificadas estratégicamente por actores bien organizados, son difíciles de detectar y mitigar. El enfoque tradicional de adquirir nuevas herramientas para cada amenaza o usar un SIEM para recopilar registros a menudo resulta en una sobrecarga de falsos positivos, consumiendo un tiempo valioso para los equipos de seguridad.

Las herramientas de seguridad eficaces deben ofrecer protección sólida, integrarse sin problemas con los sistemas existentes, ser escalables y proporcionar visibilidad y control completos de la infraestructura del cliente. Además, la facilidad de uso y despliegue es esencial para asegurar una transición fluida sin interrumpir las operaciones de los clientes.

En un tiempo récord de pocas semanas, Santillana Latinoamérica ha automatizado la gestión interna de sus más de 6.800 usuarios con una solución Low Code escalable y adaptable. Este sistema ha transformado un proceso que antes podía tardar hasta dos días en completarse en una tarea prácticamente instantánea.

La integración de Power Platform y Microsoft 365 en la nube de Microsoft ha optimizado la seguridad de los procesos de la compañía educativa. Axazure, el socio experto en el despliegue, ha facilitado esta transición con un enfoque ágil en Power Platform, permitiendo a Santillana Latinoamérica enfrentar ecosistemas híbridos de manera eficiente y a un costo significativamente menor que el desarrollo de una solución personalizada.

Santillana, una destacada empresa de servicios y contenidos educativos fundada en 1960 y con presencia en 19 países de América Latina, cuenta con más de 3.300 empleados y 6.800 usuarios. La anterior gestión manual de usuarios y licencias estaba limitando la agilidad operativa. Con la nueva solución, la automatización del alta, baja y modificación de usuarios no solo agiliza los procesos internos, sino que también garantiza una integración fluida con los sistemas existentes. Este avance responde a la necesidad de una herramienta escalable y adaptable que se implementara rápidamente, reduciendo el tiempo de desarrollo a menos de un tercio en comparación con métodos tradicionales y abordando los desafíos de un entorno híbrido con seguridad reforzada.

Cicerone, la innovadora solución de iUrban basada en Azure OpenAI, está transformando el turismo al ofrecer asistentes de IA generativa que operan en 95 idiomas, proporcionando información personalizada y disponible las 24 horas del día. Esta herramienta no solo facilita la interacción con los turistas, sino que también organiza sus estancias en tan solo 10 segundos, adaptándose a cada fase del viaje.

Además de mejorar la experiencia del visitante, Cicerone permite a los destinos turísticos analizar datos para optimizar estrategias y ofertas, cumpliendo con las regulaciones de protección de datos europeas mediante el uso de Azure OpenAI. Ciudades como Madrid, Murcia e Ibiza ya han incorporado esta solución revolucionaria, que también se ha extendido a oficinas de turismo virtuales en Mogán y el Cabildo de Lanzarote.

iUrban, una startup malagueña especializada en digitalización turística, ha liderado esta transformación, reemplazando las oficinas de turismo tradicionales y ofreciendo una experiencia personalizada y segura en 95 idiomas. Con el creciente papel de la IA en el sector, Cicerone redefine cómo los viajeros planifican, reservan y disfrutan de sus viajes, marcando un hito en la evolución del turismo.