Kategorie: Amazon AWS

Amazon Aurora serverless now supports up to 384 ACUs

Amazon Aurora serverless now supports database capacity of up to 384 Aurora Capacity Units (ACUs) for Aurora PostgreSQL-Compatible Edition, a 50% increase from the previous maximum of 256 ACUs. Each ACU provides approximately 2 gibibytes (GiB) of memory with corresponding CPU and networking, so a single Aurora serverless database can now scale up to 768 GiB of memory. When the workload completes, Aurora serverless automatically scales down to zero. This makes Aurora serverless especially well-suited for agentic AI applications, which typically have bursts of activity, long idle windows, and unpredictable traffic patterns. Aurora serverless handles all of it automatically, scaling capacity with your agents, so you only pay for what you use.
With 384 ACU support, customers can now use Aurora serverless for even more demanding workloads while continuing to scale down to zero when not in use, paying only for the capacity consumed. For new clusters, select a supported Aurora PostgreSQL engine version and set your maximum capacity up to 384 ACUs. For existing clusters on Platform Version 4, simply modify the cluster to update the maximum capacity setting (and the engine version if needed). There will be no downtime if your database is already on Platform Version 4. For existing clusters on older platform versions, upgrade to Platform Version 4 through a pending maintenance action. 384 ACU support is available for Aurora PostgreSQL versions 18.4, 17.10, 16.14, 15.18, and 14.23, running on Platform Version 4.
To learn more, see Aurora serverless capacity in the Amazon Aurora User Guide. For pricing details and Region availability, visit Amazon Aurora Pricing.  To get started, create or modify an Aurora serverless database in the AWS Management Console.
Quelle: aws.amazon.com

SageMaker MLflow Apps now support MLflow 3.16

Amazon SageMaker AI MLflow Apps now support MLflow 3.16, bringing a redesigned tracing experience and faster experiment tracking to your generative AI and machine learning workflows.
MLflow 3.16 improves debugging and analysis of generative AI applications and agents. The redesigned trace explorer is now the default, with adjustable row height, reorderable columns, custom columns from any trace tag or metadata field. This release also accelerates metric charts for experiments with large metric histories.
New MLflow Apps are created on MLflow 3.16 by default. Existing MLflow Apps will be upgraded to MLflow 3.16 during an upcoming maintenance window, with no action required.
MLflow 3.16 is available in all AWS Regions where MLflow Apps are available. To learn more, visit the SageMaker AI MLflow developer guide.
Quelle: aws.amazon.com

Amazon ECS now supports GPU sharing with NVIDIA Multi-Process Service (MPS)

Amazon Elastic Container Service (Amazon ECS) now supports GPU sharing with NVIDIA Multi-Process Service (MPS), enabling you to run multiple Amazon ECS tasks concurrently on a single NVIDIA GPU. GPU sharing helps you increase GPU utilization and reduce infrastructure costs for workloads that use only part of a GPU’s capacity, such as small-model inference, AV/ADAS simulations, embedding generation, and multi-model serving.
With GPU sharing, you can pack more workloads onto each GPU instance without changing your application code. You define the GPU memory each container needs and can optionally set a maximum share of GPU compute, and Amazon ECS places tasks on GPUs with available capacity as your services scale and deploy. GPU sharing is supported on Amazon ECS Managed Instances and Amazon ECS on Amazon EC2. On Amazon ECS Managed Instances, Amazon ECS also monitors the MPS control daemon that enables sharing on each instance. If the daemon stops or becomes unhealthy, Amazon ECS automatically replaces the instance and moves your tasks to healthy capacity, so your GPU workloads keep running without manual intervention.
To get started, use the AWS Management Console, AWS CLI, AWS SDKs, AWS CloudFormation, or other infrastructure-as-code tools to create a capacity provider with NVIDIA GPU instances, set the GPU sharing strategy and memory in the container definition of your task definition, and create a service on that capacity provider. This capability is available in all AWS commercial and AWS GovCloud (US) Regions. To learn more, visit the Amazon ECS GPU sharing documentation.
Quelle: aws.amazon.com

Amazon IVS Low-Latency Streaming now provides a failover slate

Amazon Interactive Video Service (Amazon IVS) Low-Latency Streaming now provides a failover slate, an animated image that IVS displays to viewers when a broadcaster’s stream is unexpectedly interrupted. Instead of a buffering wheel or frozen frame, viewers see the slate until the broadcaster reconnects. Live playback then resumes automatically, without viewers needing to refresh their player.
The failover slate helps you retain your audience during momentary disruptions and is ideal for broadcasters streaming from mobile devices or from locations with unreliable connectivity. You can enable the failover slate on new or existing channels and monitor slate activity with Amazon EventBridge events and Amazon CloudWatch metrics.
Amazon IVS is a managed live streaming solution designed to make low-latency or real-time video available to viewers around the world. Visit the AWS Region table for a full list of AWS Regions where the Amazon IVS console and APIs for control and creation of video streams are available.
To learn more, see the Amazon IVS Failover Slate documentation.
Quelle: aws.amazon.com

Amazon ECR now supports replication backfill for existing container images

Amazon Elastic Container Registry (Amazon ECR) now supports replication backfill, enabling you to replicate existing images in your private registry to your configured replication registry destinations. Previously, replication only applied to images pushed after the replication configuration was enabled, requiring customers to manually retag images to trigger replication of preexisting images. With replication backfill, you can now replicate your full image catalog to new or existing destinations with a single API call.
Replication backfill is useful when expanding to a new AWS Region, validating disaster recovery readiness, updating replication rules, or ensuring complete image catalogs exist across Regions for compliance purposes. You can run up to ten backfill jobs concurrently, monitor progress and per-image results through new APIs, and stop a job at any time without deleting images that have already been replicated.
Replication backfill is available in all AWS Regions where Amazon ECR is available. To learn more, refer to the Amazon ECR user guide. 
Quelle: aws.amazon.com

Amazon Corretto October 2026 Patch Updates

On October 26, 2026 Amazon announced a patch update for the following Amazon Corretto Long-Term Support (LTS) and Feature Release (FR) versions of OpenJDK. Corretto 27.0.1.0.11, 25.0.5.0.10, 21.0.13.0.11, 17.0.21.0.11, 11.0.33.0.10, and 8u512 are now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK.
Visit the Corretto home page to download Corretto 27, Corretto 25, Corretto 21, Corretto 17, Corretto 11, or Corretto 8. You can also get the updates on your Linux system by configuring a Corretto Apt, Yum, or Apk repo.
Feedback is welcomed!
Quelle: aws.amazon.com

Amazon Connect Customer expands testing and simulation to validate flow action outcomes

Amazon Connect Customer now enables you to validate the outcomes of your customer experience flow actions during testing and simulation. In addition to confirming that an action started, you can now verify the data it returns when it completes, such as a Lambda function response, an Amazon Lex resolved intent, or the variables set by an Agentic CX block. When a test fails, you can pinpoint the exact action that returned an unexpected result instead of tracing it back from a downstream step, so you can resolve issues faster and launch flow changes with greater confidence.
To learn more about this feature, see testing and simulation in the Connect Customer administrator guide. For a complete list of testing and simulation capabilities available by AWS Region, refer to Availability of Connect Customer features by Region. To learn more about Amazon Connect Customer, an agentic AI solution that helps enterprises deliver exceptional customer experiences, visit the Amazon Connect Customer website.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio adds light mode support for IDC-based domains

Today, AWS announces light mode support in Amazon SageMaker Unified Studio for Identity Center (IDC) based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes. With this launch, customers can set this visual preference in both IAM-based and IDC-based domains. 
Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over the appearance of your development environment, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can choose ‘Customize appearance’ under your Profile settings to select between visual modes including dark mode, light mode, and device setting, which matches your operating system’s setting. The setting persists across browsers and devices.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, see the Amazon SageMaker Unified Studio User Guide.
Quelle: aws.amazon.com

AWS EC2 Auto Scaling now supports Launch Before Terminate at account-level to replace instances

AWS EC2 Auto Scaling now lets you enable Launch Before Terminate as the default replacement behavior across all Auto Scaling groups in your account. When enabled, Auto Scaling launches a replacement instance and waits for it to pass health checks before terminating the instance it replaces, helping your applications maintain full capacity and availability during instance replacement events. This applies to replacement events such as instance refreshes, rebalancing, health checks, and maximum-instance-lifetime replacements.
You can enable Launch Before Terminate through AWS EC2 Auto Scaling account settings, which let you configure the behavior once at the account level instead of on each Auto Scaling group individually. You can manage the setting with the new `PutAccountSetting` and `DescribeAccountSettings` APIs, available through the AWS Management Console, AWS CLI, or AWS SDKs.
Launch Before Terminate is available in all AWS Regions at no additional cost.
To learn more, see Launch Before Terminate in the AWS EC2 Auto Scaling User Guide and AWS EC2 Auto Scaling API Reference Guide.
Quelle: aws.amazon.com

Amazon Connect Customer now measures the outcomes that drive retention and revenue

Amazon Connect Customer now scores the outcome of your customer interactions. It shows whether the customer left satisfied, is at risk of churning, made a purchase, or resolved their need in self-service. You can now see whether an interaction worked, not just how efficiently it ran. Each metric comes with a default definition you can adjust to match how your business defines success and are available both through the analytics dashboards and APIs.  
Quelle: aws.amazon.com