Amazon Elastic Container Services (Amazon ECS) now allows you to perform network fault injection experiments on your applications deployed on AWS Fargate. Fault injection experiments create disruptions to test how your applications behave, helping you improve application performance, observability, and resilience. AWS Fault Injection Service (AWS FIS) now supports 6 actions for ECS on both EC2 and Fargate: network latency, network blackhole, network packet loss, CPU stress, I/O stress, and kill process.

Developers and operators can now verify the response of their application to potential network errors, some of which may also be required for regulatory compliance. By reproducing network behaviors that may cause applications to fail, you can identify gaps in application configurations, monitoring, alarms, and operational response. Amazon ECS is introducing the ability to opt-in to allow tasks to use a fault injector such as AWS FIS to perform network experiments for increasing network latency, increasing packet loss, and blackhole port testing (dropping inbound or outbound traffic) to test how your applications perform, in addition to existing resource stress experiments.

The new experience is now automatically enabled in all AWS Regions and integration with the AWS Fault Injection Service in those regions where AWS FIS is available.

For more details, go to Amazon ECS fault Injection documentation and the AWS FIS user guide.
 

 

​Amazon Elastic Container Services (Amazon ECS) now allows you to perform network fault injection experiments on your applications deployed on AWS Fargate. Fault injection experiments create disruptions to test how your applications behave, helping you improve application performance, observability, and resilience. AWS Fault Injection Service (AWS FIS) now supports 6 actions for ECS on both EC2 and Fargate: network latency, network blackhole, network packet loss, CPU stress, I/O stress, and kill process. Developers and operators can now verify the response of their application to potential network errors, some of which may also be required for regulatory compliance. By reproducing network behaviors that may cause applications to fail, you can identify gaps in application configurations, monitoring, alarms, and operational response. Amazon ECS is introducing the ability to opt-in to allow tasks to use a fault injector such as AWS FIS to perform network experiments for increasing network latency, increasing packet loss, and blackhole port testing (dropping inbound or outbound traffic) to test how your applications perform, in addition to existing resource stress experiments. The new experience is now automatically enabled in all AWS Regions and integration with the AWS Fault Injection Service in those regions where AWS FIS is available. For more details, go to Amazon ECS fault Injection documentation and the AWS FIS user guide.    

Amazon AppStream 2.0 now offers support for Rocky Linux from CIQ, enabling ISVs and central IT organizations to stream from an RPM Package Manager (RPM) compatible environment optimized for running compute-intensive applications while leveraging the flexibility, scalability, and cost-effectiveness of the AWS Cloud. With this launch, customers have the flexibility to choose from a broader set of operating systems including Rocky Linux, Red Hat Enterprise Linux (RHEL), and Microsoft Windows.

This launch enables organizations to stream Rocky Linux apps from AppStream 2.0, helping to accelerate time to market, scaling resources up or down with demand, and managing the entire fleet centrally through the AWS Management Console. Rocky Linux on AppStream 2.0 also enables traditional desktop apps to be converted to SaaS delivery without the cost of refactoring, while pay-as-you-go billing and license-included images ensure you only pay for the resources you use.

Rocky Linux-based AppStream 2.0 instances are supported in all AWS Regions where AppStream 2.0 is available and use per second billing (with a minimum of 15 minutes). For more information, see Amazon AppStream 2.0 pricing.

To get started with Rocky Linux on AppStream 2.0, sign in to the AWS Management Console and open the AppStream 2.0 Console. For more information, see the Amazon AppStream 2.0 Administrator Guide.
 

 

​Amazon AppStream 2.0 now offers support for Rocky Linux from CIQ, enabling ISVs and central IT organizations to stream from an RPM Package Manager (RPM) compatible environment optimized for running compute-intensive applications while leveraging the flexibility, scalability, and cost-effectiveness of the AWS Cloud. With this launch, customers have the flexibility to choose from a broader set of operating systems including Rocky Linux, Red Hat Enterprise Linux (RHEL), and Microsoft Windows. This launch enables organizations to stream Rocky Linux apps from AppStream 2.0, helping to accelerate time to market, scaling resources up or down with demand, and managing the entire fleet centrally through the AWS Management Console. Rocky Linux on AppStream 2.0 also enables traditional desktop apps to be converted to SaaS delivery without the cost of refactoring, while pay-as-you-go billing and license-included images ensure you only pay for the resources you use. Rocky Linux-based AppStream 2.0 instances are supported in all AWS Regions where AppStream 2.0 is available and use per second billing (with a minimum of 15 minutes). For more information, see Amazon AppStream 2.0 pricing. To get started with Rocky Linux on AppStream 2.0, sign in to the AWS Management Console and open the AppStream 2.0 Console. For more information, see the Amazon AppStream 2.0 Administrator Guide.    

Amazon RDS for MySQL now supports MySQL Innovation Release 9.1 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases.

MySQL 9.1 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next major & minor release, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.1 release notes for more details about this release.

The Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment.

Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region.

 

​Amazon RDS for MySQL now supports MySQL Innovation Release 9.1 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases. MySQL 9.1 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next major & minor release, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.1 release notes for more details about this release. The Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment. Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region.  

Stability AI’s Stable Diffusion 3.5 Large (SD3.5 Large) is now available in Amazon Bedrock. SD3.5 Large is an advanced text-to-image model featuring 8.1 billion parameters. Trained on Amazon SageMaker HyperPod, this powerful model will enable AWS customers to generate high-quality, 1-megapixel images from text descriptions with superior accuracy and creative control.

The model excels at creating diverse, high-quality images across multiple styles, making it valuable for media, gaming, advertising, ecommerce, corporate training, retail, and education industries. Its enhanced capabilities include exceptional photorealism with detailed 3D imagery, superior handling of multiple subjects in complex scenes, and improved human anatomy rendering. The model also generates representative images with diverse skin tones and features without requiring extensive prompting. Today, Stable Image Ultra in Amazon Bedrock has been updated to include Stable Diffusion 3.5 Large in the model’s underlying architecture.

Stable Diffusion 3.5 Large is now available in Amazon Bedrock in the US West (Oregon) AWS region. To learn more read the AWS News Blog or visit the Stability AI in Amazon Bedrock product page, and documentation. To get started with SD3.5 Large, visit the Amazon Bedrock console.

 

​Stability AI’s Stable Diffusion 3.5 Large (SD3.5 Large) is now available in Amazon Bedrock. SD3.5 Large is an advanced text-to-image model featuring 8.1 billion parameters. Trained on Amazon SageMaker HyperPod, this powerful model will enable AWS customers to generate high-quality, 1-megapixel images from text descriptions with superior accuracy and creative control.
The model excels at creating diverse, high-quality images across multiple styles, making it valuable for media, gaming, advertising, ecommerce, corporate training, retail, and education industries. Its enhanced capabilities include exceptional photorealism with detailed 3D imagery, superior handling of multiple subjects in complex scenes, and improved human anatomy rendering. The model also generates representative images with diverse skin tones and features without requiring extensive prompting. Today, Stable Image Ultra in Amazon Bedrock has been updated to include Stable Diffusion 3.5 Large in the model’s underlying architecture.
Stable Diffusion 3.5 Large is now available in Amazon Bedrock in the US West (Oregon) AWS region. To learn more read the AWS News Blog or visit the Stability AI in Amazon Bedrock product page, and documentation. To get started with SD3.5 Large, visit the Amazon Bedrock console.  

AWS Partner Central Analytics now provides insights for resell revenue for AWS Partners participating in the AWS Solution Provider or Distribution Programs. The new data helps Partners gain visibility into amortized revenue generated through resellers. With this new resell revenue section in Partner Central Analytics, Partners can measure revenue by month, reseller, end customer, and geography to help define sales growth strategies.

Prior to this launch, Partners could see discounts for authorized resell services, but received limited visibility into program revenue. With this launch, the “Solution provider and distributor“ tab within Partner Central Analytics is renamed to ”Channel“, delivering four new visualizations, providing Partners with a view into amortized resell revenue by program revenue and net program revenue. Key new features include amortized Partner revenue tracking to measure customer impact, monthly refresh schedule to include previous month’s data, and comprehensive coverage of resell revenue on authorized services. This update gives Partners customer level insights, helping Partners understand spending patterns across different regions. This helps Partners decide where and how to invest to expand their market reach. Also, these new insights help Partners better track revenue generated before and after program discounts.

Approved users of an AWS Partner organization at either the Validated or Differentiated stage can access the new datasets through the Analytics tab in AWS Partner Central.

To learn more about resell revenue available in the analytics dashboard, log in to AWS Partner Central and explore the Analytics and Insights User Guide

 

​AWS Partner Central Analytics now provides insights for resell revenue for AWS Partners participating in the AWS Solution Provider or Distribution Programs. The new data helps Partners gain visibility into amortized revenue generated through resellers. With this new resell revenue section in Partner Central Analytics, Partners can measure revenue by month, reseller, end customer, and geography to help define sales growth strategies. Prior to this launch, Partners could see discounts for authorized resell services, but received limited visibility into program revenue. With this launch, the “Solution provider and distributor“ tab within Partner Central Analytics is renamed to ”Channel“, delivering four new visualizations, providing Partners with a view into amortized resell revenue by program revenue and net program revenue. Key new features include amortized Partner revenue tracking to measure customer impact, monthly refresh schedule to include previous month’s data, and comprehensive coverage of resell revenue on authorized services. This update gives Partners customer level insights, helping Partners understand spending patterns across different regions. This helps Partners decide where and how to invest to expand their market reach. Also, these new insights help Partners better track revenue generated before and after program discounts. Approved users of an AWS Partner organization at either the Validated or Differentiated stage can access the new datasets through the Analytics tab in AWS Partner Central. To learn more about resell revenue available in the analytics dashboard, log in to AWS Partner Central and explore the Analytics and Insights User Guide  

Meta’s Llama 3.3 70B model is now available in Amazon Bedrock. Llama 3.3 70B represents a significant advancement in model efficiency and performance optimization. This instruction-tuned model delivers impressive capabilities across diverse tasks, including multilingual dialogue, text summarization, and complex reasoning. Llama 3.3 70B is a text-only instruction-tuned model that provides enhanced performance relative to Llama 3.1 70B–and to Llama 3.2 90B when used for text-only applications.

The new model delivers similar performance to Llama 3.1 405B, while requiring only a fraction of the computational resources. Llama 3.3 demonstrates substantial improvements in reasoning, mathematical understanding, general knowledge, and instruction following. Its comprehensive training enables robust language understanding across multiple domains. You can use Llama 3.3 for enterprise applications, content creation, and advanced research initiatives. The model supports multiple languages and outperforms many existing conversational models on industry standard benchmarks. It also supports the ability to leverage model outputs to improve other models including synthetic data generation and distillation. Llama 3.3 provides an accessible and powerful generative AI solution for businesses seeking high-quality, efficient language model capabilities.

Meta’s Llama 3.3 70B model is available in Amazon Bedrock in the US East (Ohio) Region, and in the US East (N. Virginia) and US West (Oregon) Regions via cross-region inference. To learn more, visit the Llama product page and documentation. To get started with Llama 3.3 70B in Amazon Bedrock, visit the Amazon Bedrock console.
 

 

​Meta’s Llama 3.3 70B model is now available in Amazon Bedrock. Llama 3.3 70B represents a significant advancement in model efficiency and performance optimization. This instruction-tuned model delivers impressive capabilities across diverse tasks, including multilingual dialogue, text summarization, and complex reasoning. Llama 3.3 70B is a text-only instruction-tuned model that provides enhanced performance relative to Llama 3.1 70B–and to Llama 3.2 90B when used for text-only applications. The new model delivers similar performance to Llama 3.1 405B, while requiring only a fraction of the computational resources. Llama 3.3 demonstrates substantial improvements in reasoning, mathematical understanding, general knowledge, and instruction following. Its comprehensive training enables robust language understanding across multiple domains. You can use Llama 3.3 for enterprise applications, content creation, and advanced research initiatives. The model supports multiple languages and outperforms many existing conversational models on industry standard benchmarks. It also supports the ability to leverage model outputs to improve other models including synthetic data generation and distillation. Llama 3.3 provides an accessible and powerful generative AI solution for businesses seeking high-quality, efficient language model capabilities. Meta’s Llama 3.3 70B model is available in Amazon Bedrock in the US East (Ohio) Region, and in the US East (N. Virginia) and US West (Oregon) Regions via cross-region inference. To learn more, visit the Llama product page and documentation. To get started with Llama 3.3 70B in Amazon Bedrock, visit the Amazon Bedrock console.    

Amazon Connect now supports multi-party chat, allowing up to 4 additional agents to join an ongoing chat conversation, making it easier to collaborate and resolve customer issues quickly. For example, agents can add a supervisor or subject matter experts to join the chat, ensuring customers receive accurate and timely support.

Multi-party chat can be enabled within the AWS Console. Once enabled, agents can simply use a Quick Connect to invite additional agents to an ongoing chat. This feature is available in all commercial AWS regions where Amazon Connect is available. To learn more and get started, please refer to the help documentation or visit the Amazon Connect website.
 

 

​Amazon Connect now supports multi-party chat, allowing up to 4 additional agents to join an ongoing chat conversation, making it easier to collaborate and resolve customer issues quickly. For example, agents can add a supervisor or subject matter experts to join the chat, ensuring customers receive accurate and timely support. Multi-party chat can be enabled within the AWS Console. Once enabled, agents can simply use a Quick Connect to invite additional agents to an ongoing chat. This feature is available in all commercial AWS regions where Amazon Connect is available. To learn more and get started, please refer to the help documentation or visit the Amazon Connect website.    

AWS ParallelCluster 3.12 is now generally available. This release makes it possible to include Lustre and NVIDIA software components in ParallelCluster custom images. Now, you can include ParallelCluster’s recommended Nvidia drivers and CUDA libraries in custom images. This update also makes the Lustre client optional to account for scenarios where you may opt for alternative storage solutions. To enable these optional software components when creating custom images, configure the NvidiaSoftware and the LustreClient parameters in the build image configuration file when using the build-image command.

For more details on the release, review the AWS ParallelCluster 3.12.0 release notes.

AWS ParallelCluster is a fully-supported and maintained open-source cluster management tool that enables R&D customers and their IT administrators to operate high-performance computing (HPC) clusters on AWS. AWS ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific, engineering, and machine-learning (ML/AI) workloads at scale on AWS.

AWS ParallelCluster is available at no additional charge in the AWS Regions listed here, and you pay only for the AWS resources needed to run your applications. To learn more about launching HPC clusters on AWS, visit the AWS ParallelCluster User Guide. To start using ParallelCluster, see the installation instructions for ParallelCluster UI and CLI.

 

​AWS ParallelCluster 3.12 is now generally available. This release makes it possible to include Lustre and NVIDIA software components in ParallelCluster custom images. Now, you can include ParallelCluster’s recommended Nvidia drivers and CUDA libraries in custom images. This update also makes the Lustre client optional to account for scenarios where you may opt for alternative storage solutions. To enable these optional software components when creating custom images, configure the NvidiaSoftware and the LustreClient parameters in the build image configuration file when using the build-image command. For more details on the release, review the AWS ParallelCluster 3.12.0 release notes. AWS ParallelCluster is a fully-supported and maintained open-source cluster management tool that enables R&D customers and their IT administrators to operate high-performance computing (HPC) clusters on AWS. AWS ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific, engineering, and machine-learning (ML/AI) workloads at scale on AWS. AWS ParallelCluster is available at no additional charge in the AWS Regions listed here, and you pay only for the AWS resources needed to run your applications. To learn more about launching HPC clusters on AWS, visit the AWS ParallelCluster User Guide. To start using ParallelCluster, see the installation instructions for ParallelCluster UI and CLI.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is now available in Asia Pacific (Malaysia) Region. Amazon MSK is a fully managed service for Apache Kafka and Kafka Connect that makes it easier for you to build and run applications that use Apache Kafka as a data store. Amazon MSK is fully compatible with Apache Kafka, which enables you to more quickly migrate your existing Apache Kafka workloads to Amazon MSK with confidence or build new ones from scratch. With Amazon MSK, you spend more time building innovative streaming applications and less time managing Kafka clusters.

Visit the AWS Regions page for all the regions where Amazon MSK is available. To get started, see the Amazon MSK Developer Guide.
 

 

​Amazon Managed Streaming for Apache Kafka (Amazon MSK) is now available in Asia Pacific (Malaysia) Region. Amazon MSK is a fully managed service for Apache Kafka and Kafka Connect that makes it easier for you to build and run applications that use Apache Kafka as a data store. Amazon MSK is fully compatible with Apache Kafka, which enables you to more quickly migrate your existing Apache Kafka workloads to Amazon MSK with confidence or build new ones from scratch. With Amazon MSK, you spend more time building innovative streaming applications and less time managing Kafka clusters. Visit the AWS Regions page for all the regions where Amazon MSK is available. To get started, see the Amazon MSK Developer Guide.    

Today, AWS IoT Device Management announces the general availability of a high-throughput connectivity status query API, allowing developers to query the latest connectivity state of IoT devices, for monitoring and management purposes. AWS IoT Device Management is a fully managed cloud service that helps you register, organize, monitor, and remotely manage Internet of Things (IoT) devices at scale.

Device connectivity status is crucial for monitoring device failures and executing remote commands. The new connectivity status API which will be available to AWS IoT Device Management Fleet Indexing customers, provides a high-throughput solution (350+ requests per second) for customers to ascertain device connectivity to the cloud. It also retrieves most recent connect or disconnect event timestamp along with disconnect reason, aiding troubleshooting activities. AWS IoT Device Management’s Fleet Indexing feature enables customers to search, group devices based on device metadata, state stored across thing registry, IoT device shadow and connectivity data sources. While existing search queries are optimized for fleet-level querying, this API is optimized for single-device connectivity queries and offers lower latency to reflect connectivity state changes. With connectivity status queries, developers can now easily support targeted device monitoring and management capabilities in their applications. For example, in automotive applications, developers can first query vehicle connectivity status using this API prior to issuing remote commands to the vehicle.

Connectivity status is available to AWS IoT Device Management Fleet indexing customers and in all AWS regions where AWS IoT Device Management is available. For more information please refer to the developer guide and API documentation.

 

​Today, AWS IoT Device Management announces the general availability of a high-throughput connectivity status query API, allowing developers to query the latest connectivity state of IoT devices, for monitoring and management purposes. AWS IoT Device Management is a fully managed cloud service that helps you register, organize, monitor, and remotely manage Internet of Things (IoT) devices at scale. Device connectivity status is crucial for monitoring device failures and executing remote commands. The new connectivity status API which will be available to AWS IoT Device Management Fleet Indexing customers, provides a high-throughput solution (350+ requests per second) for customers to ascertain device connectivity to the cloud. It also retrieves most recent connect or disconnect event timestamp along with disconnect reason, aiding troubleshooting activities. AWS IoT Device Management’s Fleet Indexing feature enables customers to search, group devices based on device metadata, state stored across thing registry, IoT device shadow and connectivity data sources. While existing search queries are optimized for fleet-level querying, this API is optimized for single-device connectivity queries and offers lower latency to reflect connectivity state changes. With connectivity status queries, developers can now easily support targeted device monitoring and management capabilities in their applications. For example, in automotive applications, developers can first query vehicle connectivity status using this API prior to issuing remote commands to the vehicle. Connectivity status is available to AWS IoT Device Management Fleet indexing customers and in all AWS regions where AWS IoT Device Management is available. For more information please refer to the developer guide and API documentation.