Today, AWS has introduced five new Elastic Fabric Adapter (EFA) metrics to enhance network observability for AI/ML and High Performance Computing (HPC) workloads. These new metrics help diagnose performance issues by tracking retransmitted packets and bytes, retransmit timeout events, impaired remote connection events, and unresponsive remote receiver events.

With these new metrics, you can monitor for network congestion or instance configuration issues, allowing for timely action to maintain application performance. The metrics are implemented as counters at the per-EFA device level, accumulating data since instance launch or the most recent driver reset. Stored in the sys filesystem, these metrics counters are accessible via the instance command line. For enhanced monitoring and alerting capabilities, you can integrate these metrics into Prometheus scripts, facilitating export to third-party tools such as Grafana for dashboard creation and alarm setting. The new metrics are available on Nitro v4 (and later) instances and require EFA installer version 1.43.0 or higher. For a full list of metrics and to learn more on how to use them, please visit the Monitor an EFA user guide. For a comprehensive list of instances built on different Nitro system versions, please refer to the AWS Nitro Systems documentation.

These new metrics are supported in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. To learn more about EFA, please visit the EFA documentation. 

 

​Today, AWS has introduced five new Elastic Fabric Adapter (EFA) metrics to enhance network observability for AI/ML and High Performance Computing (HPC) workloads. These new metrics help diagnose performance issues by tracking retransmitted packets and bytes, retransmit timeout events, impaired remote connection events, and unresponsive remote receiver events. With these new metrics, you can monitor for network congestion or instance configuration issues, allowing for timely action to maintain application performance. The metrics are implemented as counters at the per-EFA device level, accumulating data since instance launch or the most recent driver reset. Stored in the sys filesystem, these metrics counters are accessible via the instance command line. For enhanced monitoring and alerting capabilities, you can integrate these metrics into Prometheus scripts, facilitating export to third-party tools such as Grafana for dashboard creation and alarm setting. The new metrics are available on Nitro v4 (and later) instances and require EFA installer version 1.43.0 or higher. For a full list of metrics and to learn more on how to use them, please visit the Monitor an EFA user guide. For a comprehensive list of instances built on different Nitro system versions, please refer to the AWS Nitro Systems documentation. These new metrics are supported in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. To learn more about EFA, please visit the EFA documentation.   

Today, AWS announces enhanced scanning capabilities for GuardDuty Malware Protection for Amazon S3. This launch increases scanning capabilities by raising the maximum file size limit from 5GB to 100 GB. Additionally, the archive processing capacity has been expanded to handle up to 10,000 files per archive, up from the previous limit of 1,000 files.

GuardDuty Malware Protection for S3 is a fully managed threat detection service that automatically scans objects uploaded to S3 buckets and alerts customers of malware, viruses, and other malicious code before they can impact workloads or downstream processes. With this launch, GuardDuty S3 malware scanning now offers customers even better protection for large files and comprehensive archive collections stored in Amazon S3.

The enhanced scanning capabilities are automatically enabled in all AWS Regions where GuardDuty Malware Protection for S3 is supported. To learn more about GuardDuty Malware Protection for S3 and its features, please visit the AWS Documentation.

 

​Today, AWS announces enhanced scanning capabilities for GuardDuty Malware Protection for Amazon S3. This launch increases scanning capabilities by raising the maximum file size limit from 5GB to 100 GB. Additionally, the archive processing capacity has been expanded to handle up to 10,000 files per archive, up from the previous limit of 1,000 files. GuardDuty Malware Protection for S3 is a fully managed threat detection service that automatically scans objects uploaded to S3 buckets and alerts customers of malware, viruses, and other malicious code before they can impact workloads or downstream processes. With this launch, GuardDuty S3 malware scanning now offers customers even better protection for large files and comprehensive archive collections stored in Amazon S3. The enhanced scanning capabilities are automatically enabled in all AWS Regions where GuardDuty Malware Protection for S3 is supported. To learn more about GuardDuty Malware Protection for S3 and its features, please visit the AWS Documentation.  

We are excited to announce that AWS Deadline Cloud is now available in Asia Pacific (Seoul) and Europe (London). Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects for films, television, broadcasting, web content, and design. Customers can now use Deadline Cloud to scale their render farms in regions that are close to their creative teams, enabling better integration with existing AWS services and creative pipelines.

Deadline Cloud is now available in 10 AWS regions worldwide: US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Seoul, Singapore, Sydney and Tokyo), and Europe (Frankfurt, Ireland, and London). For more information about AWS Regions and where Deadline Cloud is available, see the AWS Region table. To learn more about AWS Deadline Cloud and its regional availability, visit the AWS Deadline Cloud product page or refer to the AWS Regional Services List.

 

​We are excited to announce that AWS Deadline Cloud is now available in Asia Pacific (Seoul) and Europe (London). Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects for films, television, broadcasting, web content, and design. Customers can now use Deadline Cloud to scale their render farms in regions that are close to their creative teams, enabling better integration with existing AWS services and creative pipelines. Deadline Cloud is now available in 10 AWS regions worldwide: US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Seoul, Singapore, Sydney and Tokyo), and Europe (Frankfurt, Ireland, and London). For more information about AWS Regions and where Deadline Cloud is available, see the AWS Region table. To learn more about AWS Deadline Cloud and its regional availability, visit the AWS Deadline Cloud product page or refer to the AWS Regional Services List.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M4 and M4 Pro Mac instances are now generally available (GA). M4 Mac instances offer up to 20% better application build performance compared to M2 Mac instances, while M4 Pro Mac instances deliver up to 15% better application build performance compared to M2 Pro Mac instances. These instances are ideal for building and testing applications for Apple platforms such as iOS, macOS, iPadOS, tvOS, watchOS, visionOS, and Safari.

M4 and M4 Pro Mac instances are powered by the AWS Nitro System, providing up to 10 Gbps network bandwidth and 8 Gbps of Amazon Elastic Block Store (Amazon EBS) storage bandwidth. M4 Mac instances are built on Apple M4 Mac Mini computers featuring 10‑core CPU, 10‑core GPU, 24GB unified memory, and 16‑core Neural Engine. M4 Pro Mac instances feature a 14‑core CPU, 20‑core GPU, 48GB unified memory, and 16‑core Neural Engine. Both instance families come with a new 2TB instance store volume per EC2 Mac Dedicated Host, providing low latency storage for improved caching and build/test performance.

M4 and M4 Pro Mac instances enable Apple developers to migrate their most demanding build and test workloads onto AWS and run significantly more tests in parallel using multiple Xcode simulators. This accelerates application iterations and reduces time to market. Customers now have access to the most advanced Apple silicon Macs on AWS to meet their requirements, while also enabling them to modernize their Apple CI/CD with dozens of AWS services. M4 and M4 Pro Mac instances support macOS Sequoia version 15.6 and newer AMIs (Amazon Machine Images).

Amazon EC2 M4 and M4 Pro Mac instances are available in US East (N. Virginia) and US West (Oregon). To learn more or get started, see our launch blog, Amazon EC2 Mac Instances or visit the EC2 Mac documentation reference.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M4 and M4 Pro Mac instances are now generally available (GA). M4 Mac instances offer up to 20% better application build performance compared to M2 Mac instances, while M4 Pro Mac instances deliver up to 15% better application build performance compared to M2 Pro Mac instances. These instances are ideal for building and testing applications for Apple platforms such as iOS, macOS, iPadOS, tvOS, watchOS, visionOS, and Safari. M4 and M4 Pro Mac instances are powered by the AWS Nitro System, providing up to 10 Gbps network bandwidth and 8 Gbps of Amazon Elastic Block Store (Amazon EBS) storage bandwidth. M4 Mac instances are built on Apple M4 Mac Mini computers featuring 10‑core CPU, 10‑core GPU, 24GB unified memory, and 16‑core Neural Engine. M4 Pro Mac instances feature a 14‑core CPU, 20‑core GPU, 48GB unified memory, and 16‑core Neural Engine. Both instance families come with a new 2TB instance store volume per EC2 Mac Dedicated Host, providing low latency storage for improved caching and build/test performance. M4 and M4 Pro Mac instances enable Apple developers to migrate their most demanding build and test workloads onto AWS and run significantly more tests in parallel using multiple Xcode simulators. This accelerates application iterations and reduces time to market. Customers now have access to the most advanced Apple silicon Macs on AWS to meet their requirements, while also enabling them to modernize their Apple CI/CD with dozens of AWS services. M4 and M4 Pro Mac instances support macOS Sequoia version 15.6 and newer AMIs (Amazon Machine Images).
Amazon EC2 M4 and M4 Pro Mac instances are available in US East (N. Virginia) and US West (Oregon). To learn more or get started, see our launch blog, Amazon EC2 Mac Instances or visit the EC2 Mac documentation reference.  

AWS Direct Connect now supports 4-byte Autonomous System (AS) numbers for virtual interfaces. Direct Connect uses the standard Border Gateway Protocol to provide customers with private connectivity to the AWS global network. However, customers with complex, multi-tenant network topologies or who need to maintain consistent AS numbering across their entire network can run into challenges with the maximum limit of 65,536 possible 2-byte AS numbers. With 4-byte AS numbers, customers can now use the entire range supported by RFC 6793, up to 4,294,967,294.
Support for 4-byte AS numbers is now available in all AWS regions globally and on all Direct Connect virtual interface types. To get started, visit the AWS Direct Connect Console or use the updated APIs to create virtual interfaces with the new 4-byte AS numbers. For more information, check out the AWS Direct Connect documentation.

 

​AWS Direct Connect now supports 4-byte Autonomous System (AS) numbers for virtual interfaces. Direct Connect uses the standard Border Gateway Protocol to provide customers with private connectivity to the AWS global network. However, customers with complex, multi-tenant network topologies or who need to maintain consistent AS numbering across their entire network can run into challenges with the maximum limit of 65,536 possible 2-byte AS numbers. With 4-byte AS numbers, customers can now use the entire range supported by RFC 6793, up to 4,294,967,294. Support for 4-byte AS numbers is now available in all AWS regions globally and on all Direct Connect virtual interface types. To get started, visit the AWS Direct Connect Console or use the updated APIs to create virtual interfaces with the new 4-byte AS numbers. For more information, check out the AWS Direct Connect documentation.  

Today, AWS announces remote connection from Visual Studio Code (VS Code) to Amazon SageMaker Unified Studio. This new capability allows developers to leverage their VS Code setup while accessing the scalable compute resources of Amazon SageMaker. By connecting VS Code to SageMaker Unified Studio, you can maintain your existing development workflows and configurations within a unified environment for AWS analytics and AI/ML services.

SageMaker Unified Studio, part of the next generation of Amazon SageMaker, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open Source Software) like VS Code. Starting today, you can use your customized local VS Code setup while accessing your compute resources and data in Amazon SageMaker. Authentication is simple and secure using the AWS Toolkit extension in VS Code. This integration provides a streamlined path from your local development environment to scalable infrastructure for running data processing, SQL analytics, and ML workflows.

This feature is available in all Regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the Administrator Guide and User Guide. 

 

​Today, AWS announces remote connection from Visual Studio Code (VS Code) to Amazon SageMaker Unified Studio. This new capability allows developers to leverage their VS Code setup while accessing the scalable compute resources of Amazon SageMaker. By connecting VS Code to SageMaker Unified Studio, you can maintain your existing development workflows and configurations within a unified environment for AWS analytics and AI/ML services. SageMaker Unified Studio, part of the next generation of Amazon SageMaker, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open Source Software) like VS Code. Starting today, you can use your customized local VS Code setup while accessing your compute resources and data in Amazon SageMaker. Authentication is simple and secure using the AWS Toolkit extension in VS Code. This integration provides a streamlined path from your local development environment to scalable infrastructure for running data processing, SQL analytics, and ML workflows. This feature is available in all Regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the Administrator Guide and User Guide.   

Amazon Athena announces single sign-on support for its JDBC and ODBC drivers through AWS IAM Identity Center’s trusted identity propagation. This makes it simpler for organizations to manage end-user’s access to data when using 3rd party tools and implement identity-based data governance policies with a seamless sign-on experience.

With this new capability, data teams can seamlessly access data through their preferred 3rd party tools using their organizational credentials. When analysts run queries using the updated Athena JDBC (3.6.0) and ODBC (2.0.5.0) drivers, their access permissions defined in Lake Formation are applied and their actions logged. This streamlined workflow eliminates credential management overhead while ensuring consistent security policies, allowing data teams to focus on insights rather than access management. For example, data analysts using 3rd party BI tools or SQL clients can now connect to Athena using their corporate credentials, and their access to data will be restricted based on policies defined for their respective user identity or group membership in Lake Formation.

This feature is available in regions where Amazon Athena and AWS Identity Center’s trusted identity propagation are supported. To learn more about configuring identity support when using Athena drivers, see the Amazon Athena driver documentation.

 

​Amazon Athena announces single sign-on support for its JDBC and ODBC drivers through AWS IAM Identity Center’s trusted identity propagation. This makes it simpler for organizations to manage end-user’s access to data when using 3rd party tools and implement identity-based data governance policies with a seamless sign-on experience.
With this new capability, data teams can seamlessly access data through their preferred 3rd party tools using their organizational credentials. When analysts run queries using the updated Athena JDBC (3.6.0) and ODBC (2.0.5.0) drivers, their access permissions defined in Lake Formation are applied and their actions logged. This streamlined workflow eliminates credential management overhead while ensuring consistent security policies, allowing data teams to focus on insights rather than access management. For example, data analysts using 3rd party BI tools or SQL clients can now connect to Athena using their corporate credentials, and their access to data will be restricted based on policies defined for their respective user identity or group membership in Lake Formation.
This feature is available in regions where Amazon Athena and AWS Identity Center’s trusted identity propagation are supported. To learn more about configuring identity support when using Athena drivers, see the Amazon Athena driver documentation.  

Amazon Managed Service for Prometheus collector, a fully-managed agentless collector for Prometheus metrics, adds support for vending logs to Amazon CloudWatch Logs.

With Amazon Managed Service for Prometheus collector logs you can now troubleshoot issues in your setup, from context on the Prometheus target discovery process including authentication issues to scraping process to status and errors such as timeouts to information on ingesting collected metrics to your Amazon Managed Service for Prometheus workspace, for example, remote-write failures due to workspace issues.

Amazon Managed Service for Prometheus collector logs are now generally available in all regions where Amazon Managed Service for Prometheus is available.

Please visit the Amazon CloudWatch pricing page to learn more about logs pricing. Get started with Managed Service for Prometheus collector logs by visiting our user guide.

 

​Amazon Managed Service for Prometheus collector, a fully-managed agentless collector for Prometheus metrics, adds support for vending logs to Amazon CloudWatch Logs.
With Amazon Managed Service for Prometheus collector logs you can now troubleshoot issues in your setup, from context on the Prometheus target discovery process including authentication issues to scraping process to status and errors such as timeouts to information on ingesting collected metrics to your Amazon Managed Service for Prometheus workspace, for example, remote-write failures due to workspace issues.
Amazon Managed Service for Prometheus collector logs are now generally available in all regions where Amazon Managed Service for Prometheus is available.
Please visit the Amazon CloudWatch pricing page to learn more about logs pricing. Get started with Managed Service for Prometheus collector logs by visiting our user guide.  

Amazon Elastic Container Service (Amazon ECS), a fully managed container orchestration service, now makes it easier to create and update task definitions in the AWS Management Console with generative AI assistance from Amazon Q Developer.

This new capability helps customers complete their task definitions faster and more efficiently using AI-generated code suggestions. Customers can use the inline chat capability to ask Amazon Q Developer to generate, explain, or refactor task definition JSON with a conversational interface. You can inject generated suggestions at any point in the task definition and accept or reject the changes proposed. Amazon ECS has also enhanced the existing inline suggestions feature to utilize Amazon Q Developer. Now in addition to the existing property-based inline suggestions, the Amazon Q Developer suggestions can autocomplete whole blocks of sample code.

These updates are available in regions where Amazon Q Developer is available, and can be enabled or disabled through settings in the console code editor or IAM permissions. See the AWS Developer Guide for further details.

 

​Amazon Elastic Container Service (Amazon ECS), a fully managed container orchestration service, now makes it easier to create and update task definitions in the AWS Management Console with generative AI assistance from Amazon Q Developer. This new capability helps customers complete their task definitions faster and more efficiently using AI-generated code suggestions. Customers can use the inline chat capability to ask Amazon Q Developer to generate, explain, or refactor task definition JSON with a conversational interface. You can inject generated suggestions at any point in the task definition and accept or reject the changes proposed. Amazon ECS has also enhanced the existing inline suggestions feature to utilize Amazon Q Developer. Now in addition to the existing property-based inline suggestions, the Amazon Q Developer suggestions can autocomplete whole blocks of sample code. These updates are available in regions where Amazon Q Developer is available, and can be enabled or disabled through settings in the console code editor or IAM permissions. See the AWS Developer Guide for further details.  

AWS launches LocalStack integration in Visual Studio Code (VS Code), enabling developers to easily test and debug serverless applications in their local IDE. With this new integration, developers can use LocalStack to locally emulate and test their serverless applications using familiar VS Code interface without switching between tools or managing complex setup, thus simplifying their local serverless development process.

LocalStack, an AWS Partner Network (APN) partner, enables developers to emulate AWS services such as AWS Lambda, Amazon SQS, Amazon API Gateway, and DynamoDB for local application development and testing. Previously, to use LocalStack to emulate AWS services in VS Code, developers had to manually configure ports, make code changes, and switch context between the IDE and LocalStack interface. Now, with LocalStack integration in VS Code, developers can connect to LocalStack environment from their IDE without manual configuration or code changes. This gives developers access to emulated AWS resources in the IDE, making it easy to build and test serverless applications locally. For example, they can now easily test and debug Lambda functions and their interactions with AWS services in a LocalStack emulated environment from their IDE.

This integration is now available to developers using the AWS Toolkit for VS Code (v3.74.0 or later). There is no additional cost from AWS for using this integration. To get started, follow the guided AWS Walkthrough in VS Code, which automatically installs the LocalStack CLI, guides through LocalStack account setup, and creates a LocalStack profile. Then, switch to LocalStack profile and deploy applications directly to the LocalStack environment. To learn more, visit the AWS News Blog, AWS Toolkit documentation, and the Lambda Developer Guide.

 

​AWS launches LocalStack integration in Visual Studio Code (VS Code), enabling developers to easily test and debug serverless applications in their local IDE. With this new integration, developers can use LocalStack to locally emulate and test their serverless applications using familiar VS Code interface without switching between tools or managing complex setup, thus simplifying their local serverless development process. LocalStack, an AWS Partner Network (APN) partner, enables developers to emulate AWS services such as AWS Lambda, Amazon SQS, Amazon API Gateway, and DynamoDB for local application development and testing. Previously, to use LocalStack to emulate AWS services in VS Code, developers had to manually configure ports, make code changes, and switch context between the IDE and LocalStack interface. Now, with LocalStack integration in VS Code, developers can connect to LocalStack environment from their IDE without manual configuration or code changes. This gives developers access to emulated AWS resources in the IDE, making it easy to build and test serverless applications locally. For example, they can now easily test and debug Lambda functions and their interactions with AWS services in a LocalStack emulated environment from their IDE. This integration is now available to developers using the AWS Toolkit for VS Code (v3.74.0 or later). There is no additional cost from AWS for using this integration. To get started, follow the guided AWS Walkthrough in VS Code, which automatically installs the LocalStack CLI, guides through LocalStack account setup, and creates a LocalStack profile. Then, switch to LocalStack profile and deploy applications directly to the LocalStack environment. To learn more, visit the AWS News Blog, AWS Toolkit documentation, and the Lambda Developer Guide.