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Amazon Connect Customer now supports unplanned shrinkage in agent schedules

Amazon Connect Customer now enables managers to input unplanned shrinkage in agent schedules, providing a more accurate picture of staffing due to unscheduled agent absences such as late logins or unplanned sick leave. Managers can upload unplanned shrinkage assumptions directly into agent schedules and immediately see updated scheduling metrics such as scheduled headcount, net staffing, and projected service level — each adjusted for unplanned shrinkage. For example, if 10% of the agents scheduled to work at 8 AM next Monday are expected to be unavailable due to late logins, then projected service level falls from 90% to 85% with unplanned shrinkage. This enables workforce managers to proactively address gaps in staffing that may arise due to unplanned agent unavailability, thus improving scheduling accuracy and their ability to maintain target service levels.

This feature is available in all  AWS Regions  where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click  here .

 

​Amazon Connect Customer now enables managers to input unplanned shrinkage in agent schedules, providing a more accurate picture of staffing due to unscheduled agent absences such as late logins or unplanned sick leave. Managers can upload unplanned shrinkage assumptions directly into agent schedules and immediately see updated scheduling metrics such as scheduled headcount, net staffing, and projected service level — each adjusted for unplanned shrinkage. For example, if 10% of the agents scheduled to work at 8 AM next Monday are expected to be unavailable due to late logins, then projected service level falls from 90% to 85% with unplanned shrinkage. This enables workforce managers to proactively address gaps in staffing that may arise due to unplanned agent unavailability, thus improving scheduling accuracy and their ability to maintain target service levels.

This feature is available in all  AWS Regions  where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click  here .  

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Mountpoint for Amazon S3 adds memory usage controls

Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget.

Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container’s assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.  

Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the Mountpoint GitHub repository. To learn more about Mountpoint, see the overview page and the configuration guide in the Mountpoint GitHub repository.

 

​Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget.
Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container’s assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.  
Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the Mountpoint GitHub repository. To learn more about Mountpoint, see the overview page and the configuration guide in the Mountpoint GitHub repository.  

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AWS Backup adds cross-Region backup copy and logically air-gapped vault support for Amazon DocumentDB in nine additional AWS Regions

AWS Backup now supports copying Amazon DocumentDB backups across AWS Regions and storing them in logically air-gapped vaults in nine additional AWS Regions: Asia Pacific (Hong Kong, Jakarta, Melbourne, Osaka), Europe (Spain, Stockholm, Zurich), Africa (Cape Town), and Israel (Tel Aviv).

With cross-Region copy now available in these Regions, you can copy DocumentDB backups into and out of them using backup plans or on-demand copy jobs. This helps you meet disaster recovery, business continuity, and compliance requirements. You can also copy DocumentDB backups to a logically air-gapped vault in these Regions to store immutable, isolated backups that are locked by default and encrypted using AWS owned or customer-managed keys. Share vaults for recovery using AWS Resource Access Manager (RAM) and protect vault access during account compromise with Multi-party approval, reducing recovery time from a data loss event.

To get started, visit the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs. For a complete list of supported Regions and features, visit the AWS Backup documentation. To learn more about logically air-gapped vaults, visit the product page and pricing page.

 

​AWS Backup now supports copying Amazon DocumentDB backups across AWS Regions and storing them in logically air-gapped vaults in nine additional AWS Regions: Asia Pacific (Hong Kong, Jakarta, Melbourne, Osaka), Europe (Spain, Stockholm, Zurich), Africa (Cape Town), and Israel (Tel Aviv).
With cross-Region copy now available in these Regions, you can copy DocumentDB backups into and out of them using backup plans or on-demand copy jobs. This helps you meet disaster recovery, business continuity, and compliance requirements. You can also copy DocumentDB backups to a logically air-gapped vault in these Regions to store immutable, isolated backups that are locked by default and encrypted using AWS owned or customer-managed keys. Share vaults for recovery using AWS Resource Access Manager (RAM) and protect vault access during account compromise with Multi-party approval, reducing recovery time from a data loss event.
To get started, visit the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs. For a complete list of supported Regions and features, visit the AWS Backup documentation. To learn more about logically air-gapped vaults, visit the product page and pricing page.  

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Amazon Connect Customer now supports points-based scoring in performance evaluations

Amazon Connect Customer now supports points-based scoring to evaluate human and AI agent performance, giving managers more flexibility in configuring evaluation scores. With points-based scoring, managers assign points to each criterion based on its importance to the business, and the score is the sum of the points earned. In contrast, with the existing percentage-based scoring, each question is assigned weights that need to add up to 100%. For example, a points-based evaluation might award 40 points for resolving the customer’s problem, 20 for meeting compliance requirements, and 10 for a proper greeting – without the need for scores to add up to 100. Managers control how points are awarded: combining several compliance criteria into one multiple-choice question to shorten the form, excluding the call reason from scoring, or awarding bonus points for strong customer rapport. Managers decide what counts, how much it counts, and what earns extra credit, so the score reflects what the business values most.

This feature is available in all regions where Amazon Connect Customer is offered. To learn more, please visit our documentation and our webpage.

 

​Amazon Connect Customer now supports points-based scoring to evaluate human and AI agent performance, giving managers more flexibility in configuring evaluation scores. With points-based scoring, managers assign points to each criterion based on its importance to the business, and the score is the sum of the points earned. In contrast, with the existing percentage-based scoring, each question is assigned weights that need to add up to 100%. For example, a points-based evaluation might award 40 points for resolving the customer’s problem, 20 for meeting compliance requirements, and 10 for a proper greeting – without the need for scores to add up to 100. Managers control how points are awarded: combining several compliance criteria into one multiple-choice question to shorten the form, excluding the call reason from scoring, or awarding bonus points for strong customer rapport. Managers decide what counts, how much it counts, and what earns extra credit, so the score reflects what the business values most.
This feature is available in all regions where Amazon Connect Customer is offered. To learn more, please visit our documentation and our webpage.  

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Diseñada para la complejidad de la educación: IA que entiende a su institución

Diseñada para la complejidad de la educación: IA que entiende a su institución

Tres estudiantes trabajan en sus laptops

Por: Equipo de Microsoft Education.

El uso de la IA ya está muy extendido en la educación. Según el informe de IA en la Educación de Microsoft de 2026, el 92% de los estudiantes y líderes educativos encuestados y el 88% de los educadores encuestados informaron usar IA con fines escolares.¹ La experimentación ya no es la cuestión. Lo que viene a continuación es: si la IA puede pasar de ganancias individuales de productividad a algo en lo que toda una institución pueda confiar, en la enseñanza, el aprendizaje, la investigación y las operaciones.

El 92% de los estudiantes encuestados y líderes educativos y el 88% de los educadores encuestados informaron que usaban IA con fines escolares.¹
Informe de IA en la Educación de Microsoft 2026

Lean el Microsoft Education AI Toolkit

Ese cambio es en especial desafiante en la educación. Una sola institución puede apoyar la instrucción, la evaluación, el asesoramiento, la ayuda financiera, la investigación, la seguridad y las tecnologías de la información, cada una con diferentes sistemas, registros y requisitos de acceso. Si se suman las obligaciones de privacidad del estudiante y las expectativas de integridad académica, una experiencia de IA de propósito general puede carecer del contexto y los controles necesarios para el uso institucional.

Cerrar esa brecha requiere tanto contexto institucional relevante como salvaguardas sobre cómo se accede y utiliza la información. Microsoft 365 Education está diseñado para unir esos elementos, para ayudar a las instituciones a pasar de la experimentación aislada con IA hacia un uso práctico y regulado.

Apoyo en el aula con agentes

Piensen en cómo es en realidad una semana para un educador. La planificación, la diferenciación y la evaluación están cada una en lugares diferentes, y ninguno de esos sistemas fue diseñado para comunicarse entre sí. El resultado es un trabajo repetitivo y operativo que se acumula entre un profesor y sus alumnos.

Aquí es donde los agentes cambian la ecuación. En lugar de ser otra herramienta para abrir, los agentes del aula trabajan a través de los sistemas que el educador ya utiliza, para ayudar a agilizar la planificación, la diferenciación y la evaluación como trabajos conectados en lugar de tareas separadas. Redactar versiones diferenciadas de una lección para diferentes niveles de lectura, reunir materiales alineados en una unidad y recopilar los resultados de las evaluaciones en una imagen de quién necesita ayuda, forman parte de un mismo flujo.

La medida del éxito no es la automatización en sí. Es lo que la automatización devuelve: tiempo para que los educadores lo dediquen a la enseñanza y a los estudiantes, que es donde su impacto es mayor. Esa es la promesa de la IA creada para la enseñanza y el aprendizaje, basada en flujos de trabajo instruccionales reales más que en la productividad genérica.

Conectar la experiencia del estudiante

La información fragmentada también puede crear desafíos a lo largo del recorrido del estudiante. Un estudiante que navegue por asesoramiento, ayuda financiera, servicios de accesibilidad y apoyo académico puede necesitar repetir información en varias oficinas. Cada entrega adicional puede añadir fricción en un momento en el que el apoyo oportuno y coordinado importa.

Con un contexto compartido entre los servicios estudiantiles, el apoyo puede sentirse más conectado a lo largo de todo el recorrido estudiantil. Los estudiantes se encuentran con menos traspasos y reciben ayuda que refleja su situación, desde la orientación hasta los momentos en los que pueden necesitar apoyo adicional para continuar sus estudios. Entre bastidores, el personal dispone del contexto para coordinar una respuesta más oportuna, lo que ayuda a la institución a reducir las fricciones administrativas y a apoyar mejor a cada estudiante.

El resultado que buscan las instituciones aquí no es solo la eficiencia operativa. El objetivo más amplio es reducir la fricción administrativa y ayudar al personal a ofrecer un apoyo más coordinado y oportuno durante todo el recorrido estudiantil.

La base: Inteligencia, gobernanza y confianza

Un modelo de IA capaz es solo una parte de una experiencia lista para una institución. También importan el contexto relevante, la gobernanza y la supervisión humana claramente definida.

La inteligencia proporciona contexto relevante para las experiencias de IA. Microsoft IQ es la capa de inteligencia que abarca las capacidades de IA de Microsoft, y Work IQ se basa en los datos, relaciones y flujos de trabajo de Microsoft 365 a los que un usuario puede acceder. Este contexto puede ayudar  a Microsoft 365 Copilot y a los agentes a repartir información relevante de fuentes como archivos, correos electrónicos, mensajes y reuniones dentro del entorno Microsoft 365 de la institución.

La gobernanza proviene de la plataforma Microsoft 365 que su institución ya administra: identidad, permisos, cumplimiento normativo y protección de datos, aplicadas de manera constante a Copilot y a los agentes. El conocimiento institucional del que se basa esta inteligencia son sus datos. Se mantienen en su propiedad, bajo su control y gobierno.

La confianza es lo que hace que los dos primeros sean utilizables en un colegio. Los permisos y controles de gobernanza de Microsoft 365 ayudan a limitar el acceso a la información que un usuario está autorizado a usar. Las instituciones deben configurar, supervisar y revisar las experiencias de Copilot y de los agentes de acuerdo con sus requisitos de privacidad, seguridad e integridad académica.

Juntos, cumplen lo que la educación exige a la IA: que esté construida para la enseñanza y el aprendizaje, que trabaje en el flujo educativo y no en un destino separado, y que proporcione una experiencia académica de confianza que las instituciones puedan respaldar.

Emparejamiento de capacidad con preparación

Las instituciones parten de diferentes niveles de preparación para la IA. Microsoft 365 Education ofrece varias formas de empezar y ampliar su uso con el tiempo, basadas en las prioridades, licencias, gobernanza y preparación técnica de cada institución.

Microsoft 365 Copilot Chat está incluido en Microsoft 365 y es donde muchas instituciones comienzan, para ofrecer a educadores y personal una experiencia segura y cotidiana de IA. Microsoft 365 Copilot ofrece una experiencia más rica y relevante al incorporar la IA a las aplicaciones de Microsoft 365, donde ya se realizan la enseñanza, el aprendizaje, la investigación y las operaciones. También permite a las instituciones delegar trabajos en varios pasos a los agentes, al elegir las capacidades que mejor se adapten a sus necesidades y preparación.

Esa progresión es importante porque permite a las instituciones invertir donde más se necesita capacidad, para sumar a medida que la gobernanza y la preparación maduren en lugar de comprometerse en todas partes a la vez.

Hay una lógica similar detrás de cómo abordamos los modelos. La elección del modelo es otra consideración. En las experiencias compatibles con Microsoft 365 Copilot, las organizaciones pueden tener acceso a diferentes modelos para distintos tipos de trabajo, sujetos a la disponibilidad y los controles de administrador. La pregunta más útil no es tan solo qué modelo es el más reciente, sino cuál experiencia respaldada se adapta mejor a la tarea, los requisitos de la institución y los términos aplicables para el procesamiento de datos.

Por dónde empezar

Nada de esto requiere un gran despliegue para empezar.

  • Empiecen por lo que ya está establecido. Copilot Chat está incluido en Microsoft 365, lo que significa que la mayoría de las instituciones ya tienen un lugar seguro para empezar.
  • Elijan un flujo de trabajo. Escojan una secuencia única donde el coste sea visible, como el tiempo de planificación del docente, una entrega de alumnos que pierde impulso o un proceso administrativo que consuma esfuerzo del personal, y empiecen ahí en lugar de en todas partes. 
  • Dejen que la gobernanza crezca con el uso. Amplíen sus capacidades a medida que sus controles y confianza maduren, en lugar de comprometerse con toda la institución de golpe. 

La complejidad de la educación no es algo en torno a lo que deban diseñar. Es la realidad para la que debe diseñarse la IA. Microsoft se centra en reunir contexto relevante, controles institucionales y maneras flexibles de comenzar, para que las escuelas puedan explorar la IA en términos que reflejen sus necesidades y responsabilidades.

Exploren el kit de herramientas de IA de Microsoft Education

¹ Microsoft, IA en la educación: un informe especial de Microsoft, 2026.

The post Diseñada para la complejidad de la educación: IA que entiende a su institución appeared first on Source LATAM.

 

​The post Diseñada para la complejidad de la educación: IA que entiende a su institución appeared first on Source LATAM.  

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Amazon EC2 R8id instances are now available in additional AWS Regions

Amazon Elastic Compute Cloud (Amazon EC2) R8id instances are now available in additional regions. R8id instances are now available in Asia Pacific (Mumbai, Malaysia, Sydney), Canada (Central), and Europe (Ireland, Stockholm).

R8id instances feature up to 22.8TB of NVMe SSD local instance storage, 3x more than R6id, and provide up to 3.3x higher memory bandwidth, delivering up to 43% higher performance compared to R6id instances. They are ideal for memory-intensive workloads that benefit from high-performance local storage, including in-memory databases, real-time big data analytics, large in-memory caches, and data processing applications.

Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.

Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 R8id page.

 

​Amazon Elastic Compute Cloud (Amazon EC2) R8id instances are now available in additional regions. R8id instances are now available in Asia Pacific (Mumbai, Malaysia, Sydney), Canada (Central), and Europe (Ireland, Stockholm).
R8id instances feature up to 22.8TB of NVMe SSD local instance storage, 3x more than R6id, and provide up to 3.3x higher memory bandwidth, delivering up to 43% higher performance compared to R6id instances. They are ideal for memory-intensive workloads that benefit from high-performance local storage, including in-memory databases, real-time big data analytics, large in-memory caches, and data processing applications. Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.
Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 R8id page.  

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Amazon EC2 C8id and M8id instances are now available in additional AWS Regions

Amazon Elastic Compute Cloud (Amazon EC2) C8id and M8id instances with up to 22.8 TB of local NVMe-based SSD block-level storage are now available in additional regions. C8id instances are now available in Asia Pacific (Sydney) and Canada (Central) and M8id instances are available in Asia Pacific (Mumbai) and Canada (Central).

Amazon EC2 C8id and M8id instances offer 3 times more vCPUs, memory and local storage compared to previous sixth-generation instances. These instances deliver up to 43% higher compute performance and 3.3 times more memory bandwidth compared to previous sixth-generation C6id and M6id instances. They also deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics compared to previous sixth generation instances.

  • C8id instances are ideal for compute-intensive workloads, including those that need access to high-speed, low-latency local storage like video encoding, image manipulation, and other forms of media processing.

  • M8id instances are best for workloads that require a balance of compute and memory resources along with high-speed, low-latency local block storage, including data logging, media processing, and medium-sized data stores.

Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.

Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 C8id and Amazon EC2 M8id pages.

 

​Amazon Elastic Compute Cloud (Amazon EC2) C8id and M8id instances with up to 22.8 TB of local NVMe-based SSD block-level storage are now available in additional regions. C8id instances are now available in Asia Pacific (Sydney) and Canada (Central) and M8id instances are available in Asia Pacific (Mumbai) and Canada (Central).
Amazon EC2 C8id and M8id instances offer 3 times more vCPUs, memory and local storage compared to previous sixth-generation instances. These instances deliver up to 43% higher compute performance and 3.3 times more memory bandwidth compared to previous sixth-generation C6id and M6id instances. They also deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics compared to previous sixth generation instances.

C8id instances are ideal for compute-intensive workloads, including those that need access to high-speed, low-latency local storage like video encoding, image manipulation, and other forms of media processing.
M8id instances are best for workloads that require a balance of compute and memory resources along with high-speed, low-latency local block storage, including data logging, media processing, and medium-sized data stores.

Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.
Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 C8id and Amazon EC2 M8id pages.  

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Capacity Reservation Resource Groups now support Amazon EC2 Capacity Blocks and interruptible Capacity Reservations

Starting today, you can add Amazon EC2 Capacity Blocks for ML and interruptible Capacity Reservations to Capacity Reservation Resource Groups. Amazon EC2 offers different reservation offerings such as On-Demand Capacity Reservations (ODCRs), interruptible Capacity Reservations, and Capacity Blocks for ML. Previously, a Capacity Reservation Resource Group could only include ODCRs. Now you can add any type of Capacity Reservation to a Capacity Reservation Resource Group, making it easier to launch EC2 instances across your entire portfolio of reserved capacity.

To use this feature, create a Capacity Reservation Resource Group, add any Capacity Reservation to it, and then target the group in your launch request. When using EC2 Fleet and EC2 Auto Scaling groups, you can also specify your prioritization preferences across reservation types, and configure automatic fall back to EC2 On-Demand capacity when there is no capacity remaining across your reservations.

There are no additional charges for using this feature. This feature is available in all AWS Regions where Capacity Blocks for ML and interruptible ODCRs are supported, excluding AWS GovCloud (US) and China Regions. To get started, see Capacity Reservation Resource Groups and the Capacity Reservations user guide.

 

​Starting today, you can add Amazon EC2 Capacity Blocks for ML and interruptible Capacity Reservations to Capacity Reservation Resource Groups. Amazon EC2 offers different reservation offerings such as On-Demand Capacity Reservations (ODCRs), interruptible Capacity Reservations, and Capacity Blocks for ML. Previously, a Capacity Reservation Resource Group could only include ODCRs. Now you can add any type of Capacity Reservation to a Capacity Reservation Resource Group, making it easier to launch EC2 instances across your entire portfolio of reserved capacity.
To use this feature, create a Capacity Reservation Resource Group, add any Capacity Reservation to it, and then target the group in your launch request. When using EC2 Fleet and EC2 Auto Scaling groups, you can also specify your prioritization preferences across reservation types, and configure automatic fall back to EC2 On-Demand capacity when there is no capacity remaining across your reservations.
There are no additional charges for using this feature. This feature is available in all AWS Regions where Capacity Blocks for ML and interruptible ODCRs are supported, excluding AWS GovCloud (US) and China Regions. To get started, see Capacity Reservation Resource Groups and the Capacity Reservations user guide.  

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AWS Lambda MicroVMs now supports AWS PrivateLink

AWS Lambda MicroVMs now supports AWS PrivateLink, enabling private connectivity to Lambda MicroVMs directly from Amazon Virtual Private Cloud (VPC) resources without exposing traffic to the public internet. This launch helps regulated workloads in financial services, healthcare, and government meet strict network isolation requirements when building with Lambda MicroVMs. 

PrivateLink VPC Endpoints deliver private connectivity to AWS services from your VPC, ensuring that your traffic does not travel over the public internet when communicating with AWS services. Today’s launch extends this capability to Lambda MicroVMs. Developers and IT teams needing private connectivity to Lambda MicroVMs can now create PrivateLink VPC Endpoints in their VPC to call MicroVM APIs (for example, to create MicroVM images or launch MicroVMs) and to connect to each MicroVM’s HTTP endpoint.

PrivateLink VPC Endpoints for Lambda MicroVMs can be created using the AWS Management Console, AWS CLI, AWS CloudFormation, or the AWS SDKs. This feature is supported in all Regions where Lambda MicroVMs is available. Visit the AWS Capabilities by Region page for the latest region availability.  

To learn more about using PrivateLink with Lambda MicroVMs, see the developer guide. For pricing details, see AWS PrivateLink Pricing. To learn more about configuring private connectivity for AWS services, visit the AWS PrivateLink developer guide. 

 

 

​AWS Lambda MicroVMs now supports AWS PrivateLink, enabling private connectivity to Lambda MicroVMs directly from Amazon Virtual Private Cloud (VPC) resources without exposing traffic to the public internet. This launch helps regulated workloads in financial services, healthcare, and government meet strict network isolation requirements when building with Lambda MicroVMs. 
PrivateLink VPC Endpoints deliver private connectivity to AWS services from your VPC, ensuring that your traffic does not travel over the public internet when communicating with AWS services. Today’s launch extends this capability to Lambda MicroVMs. Developers and IT teams needing private connectivity to Lambda MicroVMs can now create PrivateLink VPC Endpoints in their VPC to call MicroVM APIs (for example, to create MicroVM images or launch MicroVMs) and to connect to each MicroVM’s HTTP endpoint.
PrivateLink VPC Endpoints for Lambda MicroVMs can be created using the AWS Management Console, AWS CLI, AWS CloudFormation, or the AWS SDKs. This feature is supported in all Regions where Lambda MicroVMs is available. Visit the AWS Capabilities by Region page for the latest region availability.  
To learn more about using PrivateLink with Lambda MicroVMs, see the developer guide. For pricing details, see AWS PrivateLink Pricing. To learn more about configuring private connectivity for AWS services, visit the AWS PrivateLink developer guide. 
   

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AWS IoT Core now supports native InfluxDB routing for time-series data

AWS IoT Core now supports InfluxDB rule action that routes time-series data from your Internet of Things (IoT) devices directly to InfluxDB databases, without writing custom device-side code or using intermediate cloud services. AWS IoT Core is a fully managed service that securely connects billions of IoT devices to the AWS cloud, and routes IoT device data to AWS and third-party services.

The new InfluxDB rule action automatically converts time-series data from your device to InfluxDB’s line protocol format and writes it to either an Amazon Timestream managed or a self-hosted InfluxDB cluster. The new rule action also supports the following two batching modes to help you optimize cost and throughput: device-side batching, where your devices send pre-batched payloads to AWS IoT Core; and server-side batching, where IoT rules engine aggregates individual messages before writing to InfluxDB. For example, a life sciences company can batch thousands of telemetry readings from scientific instruments at millisecond granularity and write directly to InfluxDB for monitoring, without building a custom data pipeline.

To get started, connect your IoT devices to AWS IoT Core and define an InfluxDB rule action specifying the destination database, along with authentication and batching parameters. The InfluxDB rule action is available in all AWS Global Regions where Amazon Timestream for InfluxDB is available. To learn more, visit the AWS IoT Core developer guide.

 

​AWS IoT Core now supports InfluxDB rule action that routes time-series data from your Internet of Things (IoT) devices directly to InfluxDB databases, without writing custom device-side code or using intermediate cloud services. AWS IoT Core is a fully managed service that securely connects billions of IoT devices to the AWS cloud, and routes IoT device data to AWS and third-party services.
The new InfluxDB rule action automatically converts time-series data from your device to InfluxDB’s line protocol format and writes it to either an Amazon Timestream managed or a self-hosted InfluxDB cluster. The new rule action also supports the following two batching modes to help you optimize cost and throughput: device-side batching, where your devices send pre-batched payloads to AWS IoT Core; and server-side batching, where IoT rules engine aggregates individual messages before writing to InfluxDB. For example, a life sciences company can batch thousands of telemetry readings from scientific instruments at millisecond granularity and write directly to InfluxDB for monitoring, without building a custom data pipeline.
To get started, connect your IoT devices to AWS IoT Core and define an InfluxDB rule action specifying the destination database, along with authentication and batching parameters. The InfluxDB rule action is available in all AWS Global Regions where Amazon Timestream for InfluxDB is available. To learn more, visit the AWS IoT Core developer guide.