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11 perspectivas inesperadas de la IA en el trabajo de 2024

diciembre 24, 2024

11 perspectivas inesperadas de la IA en el trabajo de 2024

Lecciones de expertos en IA, investigaciones del mundo real y avances empresariales que todo líder debería conocer.

Ilustración que muestra varios elementos que conforman el número 2024

Ilustración de Leonardo Betti

La IA comenzó a tener un impacto real en los negocios este año, al demostrar su valor como asistente personal para cada empleado y catalizador de transformación para cada organización. También fue un año de aprendizaje, ya que los líderes de opinión, la investigación del mundo real y los avances empresariales arrojaron luz sobre cómo la IA remodelará todos los aspectos del trabajo, al iluminar tanto las oportunidades como los desafíos que se avecinan. En el camino, surgieron nuevos términos, ideas y casos de uso creativos. Desde las ilustrativas («islas de inteligencia») hasta los encantadores (libros para colorear personalizados), aquí hay 11 ideas sobre la IA en el trabajo que todo líder debería conocer.

Evitar las «islas de inteligencia»

Cuando las personas comienzan a trabajar con una nueva herramienta, hay una tendencia a secuestrar el conocimiento actualizado y los flujos de trabajo de los demás. Esto crea «islas de inteligencia» que obstaculizan el intercambio de conocimientos y, con el tiempo, la transformación empresarial crítica. Con la IA, es esencial fomentar el intercambio y la discusión en toda la organización, señala A.J. Brush, gerente de productos de Microsoft Partner Group. «La gente reacciona a lo que hacen los demás, no siempre a lo que dicen», dice. «Cuando las personas ven a sus líderes usar las mismas herramientas y compartir lo que han aprendido, se emocionan por hacer lo mismo». Por lo tanto, cierre las brechas entre esas islas para construir la capacidad intelectual colectiva de su organización.

Lean: Cuando se trata de IA, no construyan «islas de inteligencia»

Asociarse

Durante décadas, los seres humanos han sido condicionados a trabajar con la tecnología mediante la emisión de comandos. Piensen en cómo usamos una calculadora: ingresan algunos números y símbolos, y ella responde con un resultado. La IA puede funcionar así, pero en realidad funciona mejor si la tratan como a un colega en lugar de como una calculadora. Cuando le piden que escriba un primer borrador o cree un conjunto de diapositivas para una presentación, es posible que no haga las cosas bien en el primer intento, por lo que deben iterar con ella o «co-crear». Se trata de tener una conversación, un ida y vuelta, hasta que aterricen en el lugar correcto. Y recuerden: con la IA, como con los humanos, nunca está de más decir «por favor» y «gracias».

Lean: Trabajen con IA como si fuera un colega, no una calculadora

Recuerden: BYOAI no es seguro para el trabajo

En el primer semestre de 2024, el 75% de los empleados utilizaban la IA en el trabajo. Pero sus empleadores lucharon por mantenerse al día. Nuestro Informe Anual del Índice de Tendencias Laborales 2024 reveló que casi el 80% de los usuarios de IA llevan sus propias herramientas de IA al trabajo. Ese número es aún mayor para los empleados de la Generación Z: un 85 por ciento de ellos dice que ha utilizado herramientas de IA en el trabajo que no fueron proporcionadas por su empleador. Estas herramientas «BYOAI» no autorizadas pueden ayudar a los empleados individuales a aumentar la productividad, pero no ayudan a la empresa a aprovechar los beneficios de la IA para todo el equipo y la organización. Peor aún, ponen en riesgo los datos de la empresa. Para todos los líderes, la implementación de la IA en toda la empresa debe ser una prioridad.

Lean: La IA en acción ya está aquí. Ahora viene la parte difícil

Usar la IA para «escribir en voz alta»

Todos hemos sufrido el síndrome de la pantalla en blanco, y puede suponer una gran pérdida de tiempo y productividad para los empleadores. Un equipo de la empresa de ciencia de materiales Dow no solo descubrió cómo la IA puede abordar el bloqueo del escritor, sino que también la utiliza para reinventar la forma en que producen documentos técnicos. Programan y graban reuniones para discutir un tema en profundidad, con el objetivo de entregar la transcripción a Copilot para tejer la conversación en un primer borrador claro y cohesivo. Llámenlo «escribir en voz alta». El resultado: un proceso que antes requería horas de trabajo ahora se puede realizar en 30 minutos.

Lean: Caída de datos de IA: 3 ideas clave de la investigación del mundo real sobre el uso de la IA

Piensen en la IA como… ¿MapQuest?

La IA inspira muchas metáforas, pero esta es una que nos encanta. En el podcast WorkLab, el científico jefe de Microsoft, Jaime Teevan, comparó la IA con los avances en la tecnología de mapas. Señaló que en verdad es útil recurrir a la IA para resumir documentos, pero limitar el uso de la tecnología a tareas tan básicas es algo así como los días en que imprimíamos las instrucciones de MapQuest para llevarlas con nosotros. Pensar de manera más amplia sobre el potencial de los mapas digitales al final nos dio cosas como la predicción del tráfico y las aplicaciones de viajes compartidos. Acercarse a la IA con el mismo nivel de creatividad promete desbloquear saltos de valor que son igual de transformadores.

Escuchen: El científico jefe de Microsoft habla sobre el potencial sin explotar de la IA

Además, piensen en ello como una sierra circular

La agencia de publicidad Supernatural AI integra la tecnología en todo su trabajo, desde la estrategia de marca hasta la ideación creativa. El cofundador Mike Barrett compartió una metáfora que usa para tranquilizar a las personas que se preocupan de que las máquinas se hagan cargo de sus trabajos. «La IA no viene por tu trabajo más de lo que las sierras circulares vinieron por el trabajo de los carpinteros», les dice. «¿La idea de que vas a encender algunas herramientas eléctricas, dejarlas solas en una habitación y volver a los muebles terminados por completo? Es ridículo». En su lugar, pide a la gente que piense en la IA como «una herramienta de poder para las personas creativas».

Lean: Dentro de una agencia de publicidad nativa de IA

Consideren todo su sistema de IA

Los grandes modelos de lenguaje han comenzado a proliferar y mejoran todo el tiempo. Un efecto secundario contrario a la intuición: a medida que los LLM se vuelvan más sofisticados, se convertirán en productos básicos. Como explica el CMO de IA en el trabajo de Microsoft, Jared Spataro, habrá muchos modelos estelares con menos diferenciación entre ellos. ¿Qué significa esto para las empresas? El modelo es una parte importante de un sistema de IA, pero no la única, por lo que las empresas deben centrarse más en cómo integrar estos modelos con sus propios datos y flujos de trabajo.

Lean: Los LLM se convierten en una mercancía, ¿y ahora qué? 

Aceleren la adopción de la IA y las ventas seguirán

Mientras la presidenta y directora ejecutiva de Lumen, Kate Johnson,  le contaba a la presentadora del podcast WorkLab, Molly Wood, sobre cómo utiliza la IA para lanzar la compañía de telecomunicaciones en el futuro, reveló un detalle sorprendente: cuando Lumen lanzó Copilot por primera vez, las métricas de adopción eran bajas. Johnson hizo auditar su propio uso y descubrió que se usaba la IA mucho menos de lo que pensaba. A continuación, hizo un esfuerzo adicional para utilizar Copilot y compartió su cambio en los hábitos diarios con otros líderes de Lumen, lo que impulsó la adopción primero entre los altos ejecutivos y luego en el resto de la organización. Con el tiempo, las personas informaron que ahorraban alrededor de 30 minutos al día, lo que se suma en una empresa con decenas de miles de empleados. Johnson también destacó cómo la tecnología ha transformado un departamento en particular: «La IA para las ventas ha sido enorme».

Escuchen: La directora ejecutiva de Lumen, Kate Johnson, dice que los líderes deben ser catalizadores

Adopten un enfoque funcional

Para ayudar a los líderes empresariales a comprender cómo la IA transformará de manera fundamental todas las funciones comerciales importantes, analizamos bajo el capó de Microsoft y le pedimos a siete líderes funcionales un informe de primera mano sobre cómo se desarrollan sus propios recorridos de adopción y hacia dónde creen que irán a continuación. Desde Finanzas y Recursos Humanos hasta Servicio al Cliente y Legal, compartieron lecciones aprendidas y consejos invaluables para sus pares en otras organizaciones. Una gran lección: busquen el mayor punto débil de su equipo y luego apliquen la IA para solucionarlo.

Lean: La IA ya ha comenzado a cambiar el trabajo, incluido Microsoft

Comiencen a implementar agentes de IA hoy mismo

Empresas con visión de futuro como Dow ya han comenzado a poner a trabajar a sus agentes. En 2024, la empresa comenzó a utilizar agentes para reducir costos en áreas críticas. Dow gasta varios miles de millones de dólares al año en envíos, y algunas de las miles de facturas contienen cargos inexactos que pueden acumular posibles sobrepagos significativos. «Si tuviéramos una mejor manera de evaluar y rastrear los errores de facturación, incluso una mejora del 1 por ciento significaría ahorros sustanciales», dice la directora de información y digital de Dow, Melanie Kalmar. Es el momento de que Copilot y los agentes entren en acción. Dow se asoció con Microsoft para automatizar el proceso de análisis de facturas de envío y optimizar su cadena de suministro global.

Lean: Impacto de la IA en Dow: Copilot identifica millones en ahorros de costos

No se olviden de divertirse

Una pregunta que nuestra presentadora de podcasts, Molly Wood, siempre hace a los invitados es sobre cómo usan la IA en su trabajo y vida personal. El caso de uso del fundador de Rackhouse Ventures, Kevin Novak, fue en particular el Modo Papá: «A mi hijo de tres años y medio le encanta colorear», nos dijo. Iremos al acuario o lo que sea, y luego llegaré a casa y usaré una IA de generación de imágenes para crear libros para colorear de nuestra aventura». Novak tenía ideas fascinantes sobre el potencial transformador de la IA en el trabajo, pero la idea de crear un libro para colorear a medida con un niño de pelo rizado y un «padre barbudo de talla grande» para deleitar a un niño pequeño demuestra el potencial transformador de la IA también en lo que respecta a la crianza de los hijos.

Escuchen: Un científico de datos que da prioridad a la IA habla sobre los límites actuales y el potencial futuro de la tecnología

¿Quieren más información esencial sobre la IA y el futuro del trabajo? Suscríbanse al boletín de WorkLab.

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Amazon EKS introduces programmatic access to Kubernetes version availability

Amazon Elastic Kubernetes Service (EKS) now lets you programmatically access availability of Kubernetes versions. This simplifies how you can discover and select available versions before creating or upgrading clusters.

Kubernetes evolves rapidly, introducing three minor version releases each year with new features, design updates, and bug fixes. EKS provides standard support for each minor version for 14 months after release. After standard support ends, the version enters a 12-month extended support period. EKS stops supporting Kubernetes versions 26 months after their release. With this launch, you can now programmatically retrieve Kubernetes version metadata, including support status and dates. You can also discover the EKS platform version and default Kubernetes version EKS will use when creating new clusters.

This feature is now generally available in all commercial AWS regions and the AWS GovCloud (US) Regions. To get started, see the EKS user guide and API specification.

 

​Amazon Elastic Kubernetes Service (EKS) now lets you programmatically access availability of Kubernetes versions. This simplifies how you can discover and select available versions before creating or upgrading clusters. Kubernetes evolves rapidly, introducing three minor version releases each year with new features, design updates, and bug fixes. EKS provides standard support for each minor version for 14 months after release. After standard support ends, the version enters a 12-month extended support period. EKS stops supporting Kubernetes versions 26 months after their release. With this launch, you can now programmatically retrieve Kubernetes version metadata, including support status and dates. You can also discover the EKS platform version and default Kubernetes version EKS will use when creating new clusters. This feature is now generally available in all commercial AWS regions and the AWS GovCloud (US) Regions. To get started, see the EKS user guide and API specification.  

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Amazon EC2 Im4gn Instances are now available in Europe (Spain) region

Starting today, Amazon EC2 Im4gn Instances are available in Europe (Spain) region. Im4gn instances are built on the AWS Nitro System and are powered by AWS Graviton2 processors. They feature up to 30TB of instance storage with the 2nd Generation AWS Nitro SSDs that are custom-designed by AWS to maximize the storage performance of I/O intensive workloads such as SQL/NoSQL databases, search engines, distributed file systems and data analytics which continuously read and write from the SSDs in a sustained manner. AWS Nitro SSDs enable up to 60% lower latency and up to 75% reduced latency variability in Im4gn instances compared to the third generation of storage optimized instances. These instances maximize the number of transactions processed per second (TPS) for I/O intensive workloads such as relational databases (e.g. MySQL, MariaDB, PostgreSQL), and NoSQL databases (KeyDB, ScyllaDB, Cassandra) which have medium-large size data sets and can benefit from high compute performance and high network throughput. They are also an ideal fit for search engines, and data analytics workloads requiring fast access to data sets on local storage.

The Im4gn instances also feature up to 100 Gbps networking and support for Elastic Fabric Adapter (EFA) for applications requiring high levels of inter-node communication.

Get started with Im4gn instances by visiting the AWS Management Console, AWS Command Line Interface (CLI), or AWS SDKs. To learn more, visit the Im4gn instances page.
 

 

​Starting today, Amazon EC2 Im4gn Instances are available in Europe (Spain) region. Im4gn instances are built on the AWS Nitro System and are powered by AWS Graviton2 processors. They feature up to 30TB of instance storage with the 2nd Generation AWS Nitro SSDs that are custom-designed by AWS to maximize the storage performance of I/O intensive workloads such as SQL/NoSQL databases, search engines, distributed file systems and data analytics which continuously read and write from the SSDs in a sustained manner. AWS Nitro SSDs enable up to 60% lower latency and up to 75% reduced latency variability in Im4gn instances compared to the third generation of storage optimized instances. These instances maximize the number of transactions processed per second (TPS) for I/O intensive workloads such as relational databases (e.g. MySQL, MariaDB, PostgreSQL), and NoSQL databases (KeyDB, ScyllaDB, Cassandra) which have medium-large size data sets and can benefit from high compute performance and high network throughput. They are also an ideal fit for search engines, and data analytics workloads requiring fast access to data sets on local storage. The Im4gn instances also feature up to 100 Gbps networking and support for Elastic Fabric Adapter (EFA) for applications requiring high levels of inter-node communication. Get started with Im4gn instances by visiting the AWS Management Console, AWS Command Line Interface (CLI), or AWS SDKs. To learn more, visit the Im4gn instances page.    

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Amazon ECR expands registry policy to all ECR actions

Today, Amazon Elastic Container Registry (Amazon ECR) announces registry policy v2 which now supports managing IAM permissions for all ECR API actions. This new registry policy makes it easier for customers to control usage of ECR capabilities within their accounts.

ECR registry policy allows customers to control usage of ECR private registries by granting permissions to perform registry-level actions to an AWS IAM principal. Registry policy version 1 (v1), only supported three actions: ReplicateImage, BatchImportUpstreamImage, and CreateRepository. Now, the new registry policy version 2 (v2) supports every ECR action. Using registry policy v2 makes it easier for customers to control permissions across all repositories in an ECR registry, allowing them to improve their security posture and save time versus configuring permissions individually across multiple repositories.

ECR registry policy v2 is now available for all ECR registries in all AWS commercial regions. You can migrate from registry policy v1 to v2 using the ECR management console or with the new ECR put-account-setting API. New ECR accounts will automatically use registry policy v2. To learn more about ECR’s registry policy and permissions, see our documentation.
 

 

​Today, Amazon Elastic Container Registry (Amazon ECR) announces registry policy v2 which now supports managing IAM permissions for all ECR API actions. This new registry policy makes it easier for customers to control usage of ECR capabilities within their accounts. ECR registry policy allows customers to control usage of ECR private registries by granting permissions to perform registry-level actions to an AWS IAM principal. Registry policy version 1 (v1), only supported three actions: ReplicateImage, BatchImportUpstreamImage, and CreateRepository. Now, the new registry policy version 2 (v2) supports every ECR action. Using registry policy v2 makes it easier for customers to control permissions across all repositories in an ECR registry, allowing them to improve their security posture and save time versus configuring permissions individually across multiple repositories. ECR registry policy v2 is now available for all ECR registries in all AWS commercial regions. You can migrate from registry policy v1 to v2 using the ECR management console or with the new ECR put-account-setting API. New ECR accounts will automatically use registry policy v2. To learn more about ECR’s registry policy and permissions, see our documentation.    

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Llama 3.3 70B now available on AWS via Amazon SageMaker JumpStart

AWS customers can now access the Llama 3.3 70B model from Meta through Amazon SageMaker JumpStart. The Llama 3.3 70B model balances high performance with computational efficiency. It also delivers output quality comparable to larger Llama versions while requiring significantly fewer resources, making it an excellent choice for cost-effective AI deployments.

Llama 3.3 70B features an enhanced attention mechanism that substantially reduces inference costs. Trained on approximately 15 trillion tokens, including web-sourced content and synthetic examples, the model underwent extensive supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF). This approach aligns outputs more closely with human preferences while maintaining high performance standards. According to Meta, this efficiency gain translates to nearly five times more cost-effective inference operations, making it an attractive option for production deployments.

Customers can deploy Llama 3.3 70B through the SageMaker JumpStart user interface or programmatically using the SageMaker Python SDK. SageMaker AI’s advanced inference capabilities help optimize both performance and cost efficiency for your deployments, allowing you to take full advantage of Llama 3.3 70B’s inherent efficiency while benefiting from a streamlined deployment process.

The Llama 3.3 70B model is available in all AWS Regions where Amazon SageMaker AI is available. To learn more about deploying Llama 3.3 70B on Amazon SageMaker JumpStart, see the documentation or read the blog.
 

 

​AWS customers can now access the Llama 3.3 70B model from Meta through Amazon SageMaker JumpStart. The Llama 3.3 70B model balances high performance with computational efficiency. It also delivers output quality comparable to larger Llama versions while requiring significantly fewer resources, making it an excellent choice for cost-effective AI deployments. Llama 3.3 70B features an enhanced attention mechanism that substantially reduces inference costs. Trained on approximately 15 trillion tokens, including web-sourced content and synthetic examples, the model underwent extensive supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF). This approach aligns outputs more closely with human preferences while maintaining high performance standards. According to Meta, this efficiency gain translates to nearly five times more cost-effective inference operations, making it an attractive option for production deployments. Customers can deploy Llama 3.3 70B through the SageMaker JumpStart user interface or programmatically using the SageMaker Python SDK. SageMaker AI’s advanced inference capabilities help optimize both performance and cost efficiency for your deployments, allowing you to take full advantage of Llama 3.3 70B’s inherent efficiency while benefiting from a streamlined deployment process. The Llama 3.3 70B model is available in all AWS Regions where Amazon SageMaker AI is available. To learn more about deploying Llama 3.3 70B on Amazon SageMaker JumpStart, see the documentation or read the blog.    

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Amazon Lightsail API endpoints now support connectivity over Internet Protocol version 6 (IPv6)

Amazon Lightsail API endpoints now support the IPv6 protocol, allowing you to connect over IPv6. To use this new capability, point your applications to use Amazon Lightsail’s new dual-stack endpoints. When you make a request to a dual-stack endpoint, the endpoint URL resolves to an IPv6 or an IPv4 address, depending on the protocol used by your network and client.

Public IPv4 addresses are being exhausted with the growth of the Internet. Earlier this year, Lightsail launched IPv6-only instance plans to support IPv6 adoption. With the introduction of dual-stack Lightsail API endpoints, you can now make requests to the Lightsail API from your Lightsail IPv6-only instances, or any IPv6 client.

You can use this capability with the AWS Command Line Interface (CLI) and AWS SDKs in all AWS Regions supporting Lightsail. To learn more, please see AWS Service Endpoints documentation and Lightsail Service Endpoints documentation.

 

​Amazon Lightsail API endpoints now support the IPv6 protocol, allowing you to connect over IPv6. To use this new capability, point your applications to use Amazon Lightsail’s new dual-stack endpoints. When you make a request to a dual-stack endpoint, the endpoint URL resolves to an IPv6 or an IPv4 address, depending on the protocol used by your network and client. Public IPv4 addresses are being exhausted with the growth of the Internet. Earlier this year, Lightsail launched IPv6-only instance plans to support IPv6 adoption. With the introduction of dual-stack Lightsail API endpoints, you can now make requests to the Lightsail API from your Lightsail IPv6-only instances, or any IPv6 client. You can use this capability with the AWS Command Line Interface (CLI) and AWS SDKs in all AWS Regions supporting Lightsail. To learn more, please see AWS Service Endpoints documentation and Lightsail Service Endpoints documentation.  

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Amazon Bedrock Agents, Flows, and Knowledge Bases now supports Latency Optimized Models

Amazon Bedrock Agents, Flows, and Knowledge Bases now offers support for the recently announced, in-preview, latency-optimized models via the SDK. This enhancement brings faster response times and improved responsiveness to AI applications built with Amazon Bedrock Tooling. Currently, this optimization is available for Anthropic’s Claude 3.5 Haiku model and Meta’s Llama 3.1 405B and 70B models, delivering reduced latency compared to standard models without compromising accuracy.

This update is particularly beneficial for customers developing latency-sensitive applications such as real-time customer service chatbots and interactive coding assistants. By leveraging purpose-built AI chips like AWS Trainium2 and advanced software optimizations in Amazon Bedrock, customers can now access more options to optimize their inference for specific use cases. Importantly, these capabilities can be integrated immediately into existing applications without additional setup or model fine-tuning, resulting in enhanced performance and faster response times.

The latency-optimized inference support for Amazon Bedrock Agents, Flows, and Knowledge Bases is available in the US East (Ohio) Region via cross-region inference. Customers can access these new capabilities through the Amazon Bedrock SDK via a runtime configuration, enabling them to programmatically incorporate these optimized models into their workflows and applications.

To learn more about Amazon Bedrock and its capabilities, including this new latency-optimized inference support, visit the Amazon Bedrock product page, pricing page, and documentation.
 

 

​Amazon Bedrock Agents, Flows, and Knowledge Bases now offers support for the recently announced, in-preview, latency-optimized models via the SDK. This enhancement brings faster response times and improved responsiveness to AI applications built with Amazon Bedrock Tooling. Currently, this optimization is available for Anthropic’s Claude 3.5 Haiku model and Meta’s Llama 3.1 405B and 70B models, delivering reduced latency compared to standard models without compromising accuracy. This update is particularly beneficial for customers developing latency-sensitive applications such as real-time customer service chatbots and interactive coding assistants. By leveraging purpose-built AI chips like AWS Trainium2 and advanced software optimizations in Amazon Bedrock, customers can now access more options to optimize their inference for specific use cases. Importantly, these capabilities can be integrated immediately into existing applications without additional setup or model fine-tuning, resulting in enhanced performance and faster response times. The latency-optimized inference support for Amazon Bedrock Agents, Flows, and Knowledge Bases is available in the US East (Ohio) Region via cross-region inference. Customers can access these new capabilities through the Amazon Bedrock SDK via a runtime configuration, enabling them to programmatically incorporate these optimized models into their workflows and applications. To learn more about Amazon Bedrock and its capabilities, including this new latency-optimized inference support, visit the Amazon Bedrock product page, pricing page, and documentation.    

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Amazon MSK now extends support for Graviton3 based M7G instances in Europe (Paris) region

You can now create Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters running on AWS Graviton3-based M7g instances or upgrade your existing x-86 based based M5 or T3 instances and replace them with AWS Graviton3-based M7G instances with a single click of a button in the Europe (Paris) AWS Region.

AWS Graviton3 processor based M7G instances on Amazon MSK provisioned clusters allows you to achieve up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances. Additionally, these instances lower energy consumption by up to 60% than comparable instances, making your Kafka clusters more environmentally sustainable.

Please refer to our blog for more information on the price/ performance improvements of M7g instances and the Amazon MSK pricing page for information on pricing. To get started, you can create new clusters using M7G instances or update your existing clusters to M7G brokers using the AWS Management Console, and read our developer guide for more information.
 

 

​You can now create Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters running on AWS Graviton3-based M7g instances or upgrade your existing x-86 based based M5 or T3 instances and replace them with AWS Graviton3-based M7G instances with a single click of a button in the Europe (Paris) AWS Region. AWS Graviton3 processor based M7G instances on Amazon MSK provisioned clusters allows you to achieve up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances. Additionally, these instances lower energy consumption by up to 60% than comparable instances, making your Kafka clusters more environmentally sustainable. Please refer to our blog for more information on the price/ performance improvements of M7g instances and the Amazon MSK pricing page for information on pricing. To get started, you can create new clusters using M7G instances or update your existing clusters to M7G brokers using the AWS Management Console, and read our developer guide for more information.    

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SES Mail Manager now supports full lifecycle logging

SES Mail Manager now offers end to end logging for ingress endpoints and rules engine actions. Customers have the ability to configure a wide range of monitoring options to the three standard logging destinations: CloudWatch, S3, and Firehose.

Mail Manager by design interoperates with external systems on both incoming and outgoing email flows. Customers expect to use it to gain visibility on email volumes as well as to troubleshoot configuration problems at every step in the email delivery paths. These new logging features enable both ad hoc investigations and automated alarming via standard CloudWatch tooling, ensuring that the overall integrity of Mail Manager configurations can be tracked programmatically alongside other customer infrastructure. The logging features also help troubleshooting in the event of a configuration change on a connected system, reducing the support burden and enabling more customer self-service solutions.

Mail Manager logging is available in every AWS Region where Mail Manager is launched. Customers can learn more about Mail Manager by clicking here.
 

 

​SES Mail Manager now offers end to end logging for ingress endpoints and rules engine actions. Customers have the ability to configure a wide range of monitoring options to the three standard logging destinations: CloudWatch, S3, and Firehose. Mail Manager by design interoperates with external systems on both incoming and outgoing email flows. Customers expect to use it to gain visibility on email volumes as well as to troubleshoot configuration problems at every step in the email delivery paths. These new logging features enable both ad hoc investigations and automated alarming via standard CloudWatch tooling, ensuring that the overall integrity of Mail Manager configurations can be tracked programmatically alongside other customer infrastructure. The logging features also help troubleshooting in the event of a configuration change on a connected system, reducing the support burden and enabling more customer self-service solutions. Mail Manager logging is available in every AWS Region where Mail Manager is launched. Customers can learn more about Mail Manager by clicking here.    

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AWS CloudTrail now supports Internet Protocol Version 6 (IPv6)

AWS CloudTrail introduces dual stack support for the CloudTrail API endpoints, enabling you to connect using Internet Protocol Version 6 (IPv6), Internet Protocol Version 4 (IPv4), or dual stack clients. Dual stack support is also available when you privately access the CloudTrail API endpoint from your Amazon Virtual Private Cloud (VPC) using AWS PrivateLink.

The urgency to transition to Internet Protocol version 6 (IPv6) is driven by the continued growth of internet, which is exhausting available Internet Protocol version 4 (IPv4) addresses. With simultaneous support for both IPv4 and IPv6 clients on CloudTrail endpoints, you are able to gradually transition from IPv4 to IPv6 based systems and applications, without needing to switch all over at once. This enables you to meet IPv6 compliance requirements and removes the need for expensive networking equipment to handle the address translation between IPv4 and IPv6

To learn more on best practices for configuring IPv6 in your environment, visit the whitepaper on IPv6 in AWS. Support for IPv6 on AWS CloudTrail is available in all commercial regions and the AWS GovCloud (US) Regions.

 

​AWS CloudTrail introduces dual stack support for the CloudTrail API endpoints, enabling you to connect using Internet Protocol Version 6 (IPv6), Internet Protocol Version 4 (IPv4), or dual stack clients. Dual stack support is also available when you privately access the CloudTrail API endpoint from your Amazon Virtual Private Cloud (VPC) using AWS PrivateLink. The urgency to transition to Internet Protocol version 6 (IPv6) is driven by the continued growth of internet, which is exhausting available Internet Protocol version 4 (IPv4) addresses. With simultaneous support for both IPv4 and IPv6 clients on CloudTrail endpoints, you are able to gradually transition from IPv4 to IPv6 based systems and applications, without needing to switch all over at once. This enables you to meet IPv6 compliance requirements and removes the need for expensive networking equipment to handle the address translation between IPv4 and IPv6 To learn more on best practices for configuring IPv6 in your environment, visit the whitepaper on IPv6 in AWS. Support for IPv6 on AWS CloudTrail is available in all commercial regions and the AWS GovCloud (US) Regions.