Amazon Elastic Kubernetes Service (EKS) now supports seven additional IAM condition keys for cluster creation and configuration APIs, enhancing the governance controls available through IAM policies and Service Control Policies (SCPs). Organizations managing multi-account environments require centralized mechanisms to enforce security and compliance requirements consistently across all clusters without relying on manual processes or post-deployment checks. This expansion of EKS IAM condition keys further enables proactive policy enforcement, providing organizations with more granular control to establish guardrails for cluster configurations.
Organizations can now enforce private-only API endpoints (eks:endpointPublicAccess, eks:endpointPrivateAccess), require customer-managed AWS KMS keys for secrets encryption (eks:encryptionConfigProviderKeyArns), restrict clusters to approved Kubernetes versions (eks:kubernetesVersion), mandate deletion protection for production workloads (eks:deletionProtection), specify control plane scaling tiers (eks:controlPlaneScalingTier), and enable zonal shift capabilities for high availability (eks:zonalShiftEnabled). These condition keys apply to CreateCluster, UpdateClusterConfig, UpdateClusterVersion, and AssociateEncryptionConfig APIs, integrating seamlessly with AWS Organizations SCPs for centralized governance across accounts.
The new IAM condition keys are available in all AWS Regions where Amazon EKS is available at no additional charge. To learn more about Amazon EKS IAM condition keys, see the Amazon EKS User Guide and the Service Authorization Reference for Amazon EKS. For information about implementing Service Control Policies, see the AWS Organizations documentation.
Amazon Elastic Kubernetes Service (EKS) now supports seven additional IAM condition keys for cluster creation and configuration APIs, enhancing the governance controls available through IAM policies and Service Control Policies (SCPs). Organizations managing multi-account environments require centralized mechanisms to enforce security and compliance requirements consistently across all clusters without relying on manual processes or post-deployment checks. This expansion of EKS IAM condition keys further enables proactive policy enforcement, providing organizations with more granular control to establish guardrails for cluster configurations. Organizations can now enforce private-only API endpoints (eks:endpointPublicAccess, eks:endpointPrivateAccess), require customer-managed AWS KMS keys for secrets encryption (eks:encryptionConfigProviderKeyArns), restrict clusters to approved Kubernetes versions (eks:kubernetesVersion), mandate deletion protection for production workloads (eks:deletionProtection), specify control plane scaling tiers (eks:controlPlaneScalingTier), and enable zonal shift capabilities for high availability (eks:zonalShiftEnabled). These condition keys apply to CreateCluster, UpdateClusterConfig, UpdateClusterVersion, and AssociateEncryptionConfig APIs, integrating seamlessly with AWS Organizations SCPs for centralized governance across accounts. The new IAM condition keys are available in all AWS Regions where Amazon EKS is available at no additional charge. To learn more about Amazon EKS IAM condition keys, see the Amazon EKS User Guide and the Service Authorization Reference for Amazon EKS. For information about implementing Service Control Policies, see the AWS Organizations documentation.
Amazon S3 Express One Zone, a high-performance S3 storage class for latency-sensitive applications, now supports S3 Inventory. S3 Inventory provides a scheduled alternative to S3’s synchronous List API. You can configure S3 Inventory to generate reports on a daily or weekly basis that list your stored objects within an S3 directory bucket or with a specific prefix, and their respective metadata and encryption status. You can simplify and speed up business workflows and big data jobs with S3 Inventory, and verify encryption status of your objects to meet business, compliance, and regulatory needs.
You can use the AWS CLI, AWS SDKs, or S3 API to configure a daily or weekly inventory report for all the objects within your S3 directory bucket or a subset of the objects under a shared prefix. As part of the configuration, you can specify a destination S3 bucket for your S3 Inventory report, the output file format (CSV, ORC, or Parquet), and specific object metadata necessary for your business application, such as object name, size, last modified date, storage class, multipart upload flag, and encryption status.
Amazon S3 Express One Zone, a high-performance S3 storage class for latency-sensitive applications, now supports S3 Inventory. S3 Inventory provides a scheduled alternative to S3’s synchronous List API. You can configure S3 Inventory to generate reports on a daily or weekly basis that list your stored objects within an S3 directory bucket or with a specific prefix, and their respective metadata and encryption status. You can simplify and speed up business workflows and big data jobs with S3 Inventory, and verify encryption status of your objects to meet business, compliance, and regulatory needs.
You can use the AWS CLI, AWS SDKs, or S3 API to configure a daily or weekly inventory report for all the objects within your S3 directory bucket or a subset of the objects under a shared prefix. As part of the configuration, you can specify a destination S3 bucket for your S3 Inventory report, the output file format (CSV, ORC, or Parquet), and specific object metadata necessary for your business application, such as object name, size, last modified date, storage class, multipart upload flag, and encryption status.
S3 Inventory for S3 Express One Zone is available in all AWS Regions where the storage class is available. For pricing information, visit the S3 pricing page. To learn more, visit the S3 Inventory documentation.
La Transformación Frontier impulsa el crecimiento y la innovación en distintos sectores
Por: Kathleen Mitford, vicepresidenta corporativa de marketing global de la industria.
En todos los sectores, presenciamos un cambio fundamental. Las organizaciones han comenzado a dejar atrás la experimentación con IA y sientan las bases para la Transformación Frontier: utilizar la IA para impulsar la innovación y el crecimiento. Esta evolución depende tanto de la inteligencia como de la confianza. La inteligencia es la información única humana y organizacional, el contexto y la experiencia que hacen que la IA sea relevante y esté arraigada en las realidades del trabajo. La confianza garantiza que la IA pueda escalar de manera segura y responsable. A medida que las organizaciones refuerzan estas bases, surge una nueva pregunta: ¿cómo se ve el ‘retorno’ cuando la IA pasa de la experimentación al tejido del trabajo diario?
De manera tradicional, el retorno de la inversión es una medida financiera: el crecimiento se sospesa frente al coste. Con la IA, los rendimientos aún tienen impacto financiero, pero cada vez abarcan un conjunto más amplio de resultados. A medida que las organizaciones interiorizan este cambio, ven un retorno de la inteligencia: la acumulación de beneficios en ahorro de costes, mitigación de riesgos, rendimiento, crecimiento e innovación. El impulso suele comenzar con mejoras en la eficiencia, para luego derivar en resultados de innovación y crecimiento como experiencias más personalizadas, ciclos más rápidos, mejores decisiones y nuevos productos y servicios.
En Microsoft, vemos este cambio de primera mano mientras trabajamos con miles de organizaciones de diversos sectores. Esta publicación es la primera de una serie sobre la Transformación Frontier de la industria. En los artículos que siguen, exploraré más a fondo las dimensiones clave de este cambio: cómo la IA impulsa el crecimiento y transforma la innovación en distintos sectores.
Cómo se desarrolla la Transformación Frontier en diferentes sectores
Según un estudio de IDC, el 68% de las organizaciones ya utilizan IA. Muchos reportan ganancias medibles, con un retorno medio de la inversión de 2,3 veces. Las más avanzadas entre ellas, Frontier Firms (Empresas Frontier), llevan la IA más allá, integrándola en funciones, roles y procesos para resolver desafíos de alto impacto y específicos de la industria.
El contexto industrial hace que los resultados de inversión en IA sean visibles y medibles. Muchas organizaciones ya han comenzado a ver los primeros avances de la IA en su sector, como la optimización de inventarios en el comercio minorista, menos incidentes de seguridad en la fabricación y menor carga de documentación en el sector sanitario. Pero los líderes del sector van más allá, al convertir esos avances iniciales en resultados pioneros en crecimiento e innovación. Ejemplos incluyen la interacción personalizada y el descubrimiento de productos en el comercio minorista, mayores tasas de cumplimiento de pedidos en la fabricación y nuevas vías de atención o una mejora en la retención de pacientes en el sector sanitario. Los líderes pioneros demuestran que cuando la IA está basada en las realidades del sector, las primeras victorias se convierten en un impacto acumulado a lo largo de toda la cadena de valor.
Clientes Frontier en acción
En mi puesto en Microsoft, puedo ver este cambio de cerca mientras trabajamos con miles de organizaciones de diferentes sectores. Lo que veo es que la Transformación Frontier ya ha comenzado a tomar forma en el flujo de trabajo: los procesos se rediseñan, las decisiones se aceleran y los equipos avanzan con mayor claridad y rapidez. A partir de ahí, ese impulso se convierte en nuevas fuentes de crecimiento e innovación en toda la empresa. A continuación, algunos ejemplos estupendos de lo que veo a medida que los clientes impulsan resultados en verdad pioneros.
Servicios financieros: Transformar la investigación jurídica con conocimientos impulsados por IA
El equipo legal interno de UBS debe encontrar información muy específica, como una cláusula o regulación, en una biblioteca de 26 millones de documentos legales en varios idiomas.
«Encontrar conocimientos específicos en este vasto repositorio era como encontrar un grano de arena concreto en la playa.«
Vlad Stoian, product owner del asistente legal de IA en UBS
A través de su trabajo con Microsoft Azure, UBS perfeccionó su proceso estándar y demostró innovación con el lanzamiento del Legal AI Assistant (LAIA) para ayudar a los empleados a identificar frases, cláusulas y párrafos por medio de lenguaje natural y similitud semántica, en lugar de hacerlo a través de la coincidencia de palabras clave. Los empleados de UBS ahora pueden localizar información de manera mucho más rápida y fácil que con herramientas de búsqueda anteriores.
Comercio minorista: Reinventar la experiencia del cliente con personalización impulsada por IA
Los fabricantes de chocolate y helado italianos desde 1878, Venchi, comparten la allegria (alegría) italiana en todo el mundo. Venchi creó un programa de fidelización para recopilar datos sobre los clientes a través de Dynamics 365. A partir de esta base, Venchi introduce la personalización impulsada por IA a través de las capacidades de Copilot en la app Store Commerce.
«En el futuro, imagina que nuestro asistente de ventas puede ver en la app, de Customer Insights, que el cliente compró hace un año para el cumpleaños de su esposa, y sabemos que es alérgica a los lácteos. Con Copilot, podemos acceder con facilidad a los datos de las 350 recetas de chocolate y averiguar cuáles son opciones seguras para el cliente en solo unos segundos.«
Fabio Tormen, director de información en Venchi
Venchi ahorra 1.500 horas anuales a través de la automatización del cumplimiento. Una contabilidad y gestión de inventarios más precisas redujeron el coste de los bienes vendidos en un 2% interanual. Y la inscripción fácil añadió 800.000 clientes al programa de fidelización en su primer año.
Automotriz: Aprovechar los conocimientos impulsados por IA durante el desarrollo del vehículo
Para optimizar el rendimiento de sus vehículos en desarrollo, los ingenieros de BMW deben acceder y analizar grandes cantidades de datos de telemetría de vehículos de prueba, pero solo los especialistas informáticos de BMW han podido realizar consultas, lo que ralentiza los ciclos de prueba y la innovación. Con Azure y Foundry Agent Service, BMW ofrece información 12 veces más rápido y capacita a sus ingenieros para analizar la telemetría de manera directa. También integra flujos de trabajo impulsados por IA en la investigación y desarrollo diaria, lo que acelera los ciclos de diseño y reduce las correcciones en fases avanzadas.
Cuando un ingeniero hace una pregunta—»¿Cuántas maniobras de frenado han realizado los vehículos de desarrollo en los últimos dos días?» —el sistema responde en cuestión de minutos, con gráficos y explicaciones escritas.
«Con la IA multiagente, los ingenieros no solo obtienen datos, sino que obtienen conocimientos sobre los que pueden actuar de inmediato», dice Christof Gebhart, responsable de Tecnología Avanzada de Medición de Vehículos en BMW. «En última instancia, los pasos de extracción de datos y reconocimiento de patrones pueden realizarse de manera directa, en un solo paso, y en lenguaje natural.»
Salud: Innovar con IA para ayudar a los clínicos a dedicar más tiempo a la atención al paciente
Los clínicos de Cooper University Health Care experimentaban un agotamiento significativo debido a la documentación fuera de horario. El equipo directivo buscó una solución para reducir la carga administrativa y restaurar la alegría en la práctica. Cooper implementó Microsoft Dragon Copilot, un asistente de IA para el flujo de trabajo clínico integrado con su EHR Epic, que agiliza la documentación, automatiza tareas y muestra información, lo que aumenta la eficiencia, satisfacción y atención al paciente.
Los clínicos de Cooper informan que ahorran más de cuatro minutos en tiempo de documentación por paciente, experimentan menos agotamiento y se comprometen de manera más significativa con ellos. Sus notas son más completas, la comunicación mejora y la satisfacción del paciente va en aumento. Mediante la captura ambiental de las visitas al paciente, los profesionales pueden mantener el contacto visual y relacionarse de manera más significativa con los pacientes.
«Los pacientes notan al instante que sus clínicos los miran de nuevo, que mantienen ese contacto visual. Hemos tenido varios pacientes que han comentado, oye, wow, hoy no has escrito nada. Ese es el poder de la IA. Devuelve el contacto visual a la medicina tal y como se suponía que debía practicarse.«
Snehal Gandhi, MD, vicepresidente y director de información médica (CMIO, por sus siglas en inglés) en Cooper
Servicios financieros: Empoderar decisiones más inteligentes con información en tiempo real
Los equipos de ingeniería y datos de Aon se propusieron construir una plataforma de IA segura y de nivel empresarial que pudiera operar en todas sus líneas de soluciones. El resultado fue AonGPT, un asistente de IA generativa desarrollado por completo en Microsoft Azure.
«Más de 62.000 usuarios tienen acceso a AonGPT. Unos 31.000 de ellos son usuarios activos mensuales, con más de 6,4 millones de mensajes intercambiados hasta ahora.«
Amit Gawali, jefe de ingeniería en Aon
Durante los incendios forestales de California, el equipo de modelización de catástrofes de Aon colaboró con un proveedor de imágenes satelitales para recibir múltiples actualizaciones visuales cada día. Por medio de AonGPT, el equipo escribió código para conectar esas imágenes con los datos propietarios de Aon, para producir información casi en tiempo real que ayudó a los clientes a evaluar los daños y planificar las respuestas.
Comiencen su recorrido de Transformación Frontier
La Transformación Frontier ya ha comenzado a tomar forma en diversos sectores. La cuestión ahora no es si la IA produce impacto, sino por dónde empezar.
Amazon Connect now supports the use of flow modules across all Connect flows, allowing you to reuse common logic and functionality beyond inbound customer experiences. Flow modules organize repeatable logic and create common reusable functions across the customer experiences you build with flows. For example, you can now use a module to share information about a customer’s recent transactions in an agent whisper flow, preparing the agent with relevant details and leveraging functionality that was previously only available as part of inbound flows.
Additionally, you can now use flow modules within other modules, enabling you to build complex logic by stitching together pre-built intermediary steps under a single module. For example, a credit card eligibility module can invoke other modules that check credit scores, verify income, and review payment history before making a final determination. This modular approach allows you to build reusable components that can be combined and extended as your business requirements evolve.
To learn more about these features, see the Amazon Connect Administrator Guide. To understand recent enhancements to flow module capabilities, see our AWS blog post. This feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.
Amazon Connect now supports the use of flow modules across all Connect flows, allowing you to reuse common logic and functionality beyond inbound customer experiences. Flow modules organize repeatable logic and create common reusable functions across the customer experiences you build with flows. For example, you can now use a module to share information about a customer’s recent transactions in an agent whisper flow, preparing the agent with relevant details and leveraging functionality that was previously only available as part of inbound flows. Additionally, you can now use flow modules within other modules, enabling you to build complex logic by stitching together pre-built intermediary steps under a single module. For example, a credit card eligibility module can invoke other modules that check credit scores, verify income, and review payment history before making a final determination. This modular approach allows you to build reusable components that can be combined and extended as your business requirements evolve. To learn more about these features, see the Amazon Connect Administrator Guide. To understand recent enhancements to flow module capabilities, see our AWS blog post. This feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.
AWS Clean Rooms now supports configurable Spark properties for PySpark jobs, offering customers the ability to optimize their workloads based on their performance and scale requirements. With this launch, customers can customize Spark settings such as memory overhead, task concurrency, and network timeouts for each analysis that uses PySpark, the Python API for Apache Spark. For example, a pharmaceutical research company collaborating with healthcare organizations for real-world clinical trial data can set specific memory tuning for large-scale workloads to improve performance and optimize costs.
AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
AWS Clean Rooms now supports configurable Spark properties for PySpark jobs, offering customers the ability to optimize their workloads based on their performance and scale requirements. With this launch, customers can customize Spark settings such as memory overhead, task concurrency, and network timeouts for each analysis that uses PySpark, the Python API for Apache Spark. For example, a pharmaceutical research company collaborating with healthcare organizations for real-world clinical trial data can set specific memory tuning for large-scale workloads to improve performance and optimize costs.
AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
Amazon Elastic Container Registry (Amazon ECR) now automatically discovers and syncs OCI referrers, such as image signatures, SBOMs, and attestations, from upstream registries into your Amazon ECR private repositories with its pull through cache feature.
Previously, when you listed referrers on a repository with a matching pull through cache rule, Amazon ECR would not return or sync referrers from the upstream repository. This meant that you had to manually list and fetch the upstream referrers.
With today’s launch, Amazon ECR’s pull through cache will now reach upstream during referrers API requests and automatically cache related referrer artifacts in your private repository. This enables end-to-end image signature verification, SBOM discovery, and attestation retrieval workflows to work seamlessly with pull through cache repositories without requiring any client-side workarounds.
This feature is available today in all AWS Regions where Amazon ECR pull through cache is supported. To learn more, visit the Amazon ECR documentation.
Amazon Elastic Container Registry (Amazon ECR) now automatically discovers and syncs OCI referrers, such as image signatures, SBOMs, and attestations, from upstream registries into your Amazon ECR private repositories with its pull through cache feature. Previously, when you listed referrers on a repository with a matching pull through cache rule, Amazon ECR would not return or sync referrers from the upstream repository. This meant that you had to manually list and fetch the upstream referrers. With today’s launch, Amazon ECR’s pull through cache will now reach upstream during referrers API requests and automatically cache related referrer artifacts in your private repository. This enables end-to-end image signature verification, SBOM discovery, and attestation retrieval workflows to work seamlessly with pull through cache repositories without requiring any client-side workarounds. This feature is available today in all AWS Regions where Amazon ECR pull through cache is supported. To learn more, visit the Amazon ECR documentation.
Amazon SageMaker HyperPod now supports flexible instance groups, enabling customers to specify multiple instance types and multiple subnets within a single instance group. Customers running training and inference workloads on HyperPod often need to span multiple instance types and availability zones for capacity resilience, cost optimization, and subnet utilization, but previously had to create and manage a separate instance group for every instance type and availability zone combination, resulting in operational overhead across cluster configuration, scaling, patching, and monitoring.
With flexible instance groups, you can define an ordered list of instance types using the new InstanceRequirements parameter and provide multiple subnets across availability zones in a single instance group. HyperPod provisions instances using the highest-priority type first and automatically falls back to lower-priority types when capacity is unavailable, eliminating the need for customers to manually retry across individual instance groups. Training customers benefit from multi-subnet distribution within an availability zone to avoid subnet exhaustion. Inference customers scaling manually get automatic priority-based fallback across instance types without needing to retry each instance group individually, while those using Karpenter autoscaling can reference a single flexible instance group. Karpenter automatically detects supported instance types from the flexible instance group and provisions the optimal type and availability zone based on pod requirements. You can create flexible instance groups using the CreateCluster and UpdateCluster APIs, the AWS CLI, or the AWS Management Console.
Flexible instance groups are available for SageMaker HyperPod clusters using the EKS orchestrator in all AWS Regions where SageMaker HyperPod is supported. To learn more, see Flexible instance groups.
Amazon SageMaker HyperPod now supports flexible instance groups, enabling customers to specify multiple instance types and multiple subnets within a single instance group. Customers running training and inference workloads on HyperPod often need to span multiple instance types and availability zones for capacity resilience, cost optimization, and subnet utilization, but previously had to create and manage a separate instance group for every instance type and availability zone combination, resulting in operational overhead across cluster configuration, scaling, patching, and monitoring. With flexible instance groups, you can define an ordered list of instance types using the new InstanceRequirements parameter and provide multiple subnets across availability zones in a single instance group. HyperPod provisions instances using the highest-priority type first and automatically falls back to lower-priority types when capacity is unavailable, eliminating the need for customers to manually retry across individual instance groups. Training customers benefit from multi-subnet distribution within an availability zone to avoid subnet exhaustion. Inference customers scaling manually get automatic priority-based fallback across instance types without needing to retry each instance group individually, while those using Karpenter autoscaling can reference a single flexible instance group. Karpenter automatically detects supported instance types from the flexible instance group and provisions the optimal type and availability zone based on pod requirements. You can create flexible instance groups using the CreateCluster and UpdateCluster APIs, the AWS CLI, or the AWS Management Console. Flexible instance groups are available for SageMaker HyperPod clusters using the EKS orchestrator in all AWS Regions where SageMaker HyperPod is supported. To learn more, see Flexible instance groups.
Amazon EC2 High Memory U7i-8TB instances (u7i-8tb.112xlarge) and U7i-12TB instances (u7i-12tb.224xlarge) are now available in AWS Asia Pacific (Singapore) region. U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-8tb instances offer 8TiB of DDR5 memory, and U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-8tb instances deliver 448 vCPUs; U7i-12tb instances deliver 896 vCPUs. Both instances support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 100 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
Amazon EC2 High Memory U7i-8TB instances (u7i-8tb.112xlarge) and U7i-12TB instances (u7i-12tb.224xlarge) are now available in AWS Asia Pacific (Singapore) region. U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-8tb instances offer 8TiB of DDR5 memory, and U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-8tb instances deliver 448 vCPUs; U7i-12tb instances deliver 896 vCPUs. Both instances support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 100 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.
Today, AWS Deadline Cloud announces an AI-powered troubleshooting assistant that helps you quickly diagnose and resolve render job failures. AWS Deadline Cloud is a fully managed service that simplifies render management for computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design.
Render job failures from missing assets, software errors, configuration mismatches, and resource constraints can stall production pipelines and waste compute resources. Previously, diagnosing these issues required specialized technical staff to manually parse logs and identify root causes — a process that is time-consuming, difficult to scale, and often unavailable to smaller studios. The new Deadline Cloud assistant investigates failed jobs you identify, analyzes logs and metrics, detects common issues, and provides troubleshooting recommendations based on industry best practices and a pre-trained knowledge base covering Deadline Cloud, common render farm issues, and popular digital content creation applications including Autodesk Maya, 3ds Max, VRED, Blender, SideFX Houdini, Maxon Cinema 4D, Foundry Nuke, and Adobe After Effects. The assistant runs within your AWS account using Amazon Bedrock, keeping all data and analysis within your control.
The Deadline Cloud assistant is available today in all AWS Regions where AWS Deadline Cloud is supported. Watch a demo on YouTube to see it in action, or visit the AWS Deadline Cloud documentation to learn more.
Today, AWS Deadline Cloud announces an AI-powered troubleshooting assistant that helps you quickly diagnose and resolve render job failures. AWS Deadline Cloud is a fully managed service that simplifies render management for computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design. Render job failures from missing assets, software errors, configuration mismatches, and resource constraints can stall production pipelines and waste compute resources. Previously, diagnosing these issues required specialized technical staff to manually parse logs and identify root causes — a process that is time-consuming, difficult to scale, and often unavailable to smaller studios. The new Deadline Cloud assistant investigates failed jobs you identify, analyzes logs and metrics, detects common issues, and provides troubleshooting recommendations based on industry best practices and a pre-trained knowledge base covering Deadline Cloud, common render farm issues, and popular digital content creation applications including Autodesk Maya, 3ds Max, VRED, Blender, SideFX Houdini, Maxon Cinema 4D, Foundry Nuke, and Adobe After Effects. The assistant runs within your AWS account using Amazon Bedrock, keeping all data and analysis within your control. The Deadline Cloud assistant is available today in all AWS Regions where AWS Deadline Cloud is supported. Watch a demo on YouTube to see it in action, or visit the AWS Deadline Cloud documentation to learn more.
Amazon Managed Grafana now supports creating new workspaces with Grafana version 12.4. This release includes features that were launched as a part of open source Grafana versions 11.0 to 12.4, including Drilldown apps, scenes powered dashboards, variables in transformations, visualization enhancements, and new features with the Amazon CloudWatch plugin.
Queryless Drilldown apps enable customers to perform point-and-click exploration of Prometheus metrics, Loki logs, Tempo traces, and Pyroscope profiles. The Scenes-powered rendering engine boosts dashboard performance. Amazon CloudWatch Logs adds support for PPL and SQL queries, cross-account Metrics Insights, and log anomaly detection. The rebuilt table visualization improves performance with CSS cell styling and interactive Actions buttons, while trendline transformations and navigation bookmarks enhance data exploration. Grafana 12.4 is supported in all AWS regions where Amazon Managed Grafana is generally available.
You can create a new Amazon Managed Grafana workspace from the AWS Console, SDK, or CLI. To explore the complete list of new features, please refer to the user documentation. Follow the instructions here to create workspaces with version 12.4. To learn more about Amazon Managed Grafana features and its pricing, visit the product page and pricing page.
Amazon Managed Grafana now supports creating new workspaces with Grafana version 12.4. This release includes features that were launched as a part of open source Grafana versions 11.0 to 12.4, including Drilldown apps, scenes powered dashboards, variables in transformations, visualization enhancements, and new features with the Amazon CloudWatch plugin.
Queryless Drilldown apps enable customers to perform point-and-click exploration of Prometheus metrics, Loki logs, Tempo traces, and Pyroscope profiles. The Scenes-powered rendering engine boosts dashboard performance. Amazon CloudWatch Logs adds support for PPL and SQL queries, cross-account Metrics Insights, and log anomaly detection. The rebuilt table visualization improves performance with CSS cell styling and interactive Actions buttons, while trendline transformations and navigation bookmarks enhance data exploration. Grafana 12.4 is supported in all AWS regions where Amazon Managed Grafana is generally available.
You can create a new Amazon Managed Grafana workspace from the AWS Console, SDK, or CLI. To explore the complete list of new features, please refer to the user documentation. Follow the instructions here to create workspaces with version 12.4. To learn more about Amazon Managed Grafana features and its pricing, visit the product page and pricing page.
