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Amazon Managed Grafana now supports dual-stack connectivity (IPv6 and IPv4)

Amazon Managed Grafana now supports dual-stack connectivity, enabling workspaces to communicate over both Internet Protocol version 4 (IPv4) and Internet Protocol version 6 (IPv6). Dual-stack mode is available for workspaces running Grafana version 10.4 or later.
With dual-stack support, customers can simplify their network stack by eliminating the need to manage overlapping address spaces in their VPCs. Customers migrating to IPv6 can connect to their Grafana workspaces over IPv6 while maintaining IPv4 compatibility, and those not yet on IPv6 can continue using IPv4-only connections. This is especially beneficial as the continued growth of the internet exhausts available IPv4 addresses. 
Support for dual-stack connectivity on Amazon Managed Grafana is available in all regions where the service is generally available. To get started, update your workspace configuration via the Amazon Managed Grafana console, API, or CLI. For more information, see the Amazon Managed Grafana User Guide. To learn more about best practices for configuring IPv6 in your environment, visit the whitepaper on IPv6 in AWS.
Quelle: aws.amazon.com

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Amazon ECS introduces pause and continue controls for service deployments

Amazon Elastic Container Service (Amazon ECS) now enables you to pause service deployments at critical stages during deployment progression and continue deployments when ready. You can use these pause points to introduce manual decision points and interactive controls into your deployments for scenarios such as manual approval workflows, operational checks, integration tests, or custom automation, while continuing to use native Amazon ECS deployment strategies with managed traffic shifting, bake times, fast rollbacks, CloudWatch alarms, and deployment circuit breaker.
With this launch, you can configure a new PAUSE deployment lifecycle hook as part of your Amazon ECS service deployment configuration. When a deployment reaches a configured pause point, Amazon ECS pauses deployment progression and emits Amazon EventBridge events that you can use to trigger automation workflows, approval systems, or external validation processes. You can then continue or roll back the deployment using the new ContinueServiceDeployment API. With pause hooks, you can configure timeout durations up to 14 days and timeout actions to automatically continue or roll back the deployment if no action is received.
You can configure pause hooks for rolling, blue/green, linear, and canary deployment strategies using the Amazon ECS Console, AWS CLI, AWS SDKs, AWS CloudFormation, AWS CDK, and Terraform. You can use the ContinueServiceDeployment API through the Amazon ECS Console, AWS CLI, and AWS SDKs. This feature is available in all AWS commercial and AWS GovCloud (US) Regions. To learn more, see our documentation on pause hooks for service deployments and continuing service deployments.
Quelle: aws.amazon.com

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Amazon MWAA now supports Apache Airflow 3.2

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 3.2, the latest major release of the popular open-source workflow orchestration framework. Amazon MWAA is a managed service that lets you run Apache Airflow at scale without managing the underlying infrastructure. This release brings new data-aware scheduling capabilities and developer productivity improvements to teams building and operating data pipelines on AWS.
With Apache Airflow 3.2, you can now use asset partitioning to trigger downstream DAGs based on specific slices of data, such as a date-partitioned S3 path, rather than an entire asset, giving data engineering teams more precise control over pipeline execution. This release also expands Human-in-the-Loop (HITL) capabilities with a full audit history view for approvals, HITL support for the AgenticOperator, and synchronous callback support for Deadline Alerts. Additional improvements include Grid View virtualization for faster rendering of large DAGs, full XCom management from the Airflow UI, and async callable support in PythonOperator..
You can launch a new Apache Airflow 3.2 environment on Amazon MWAA, or upgrade from 2.11 or later, with just a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 3.2 visit the Amazon MWAA documentation, and the Apache Airflow 3.2 change log in the Apache Airflow documentation. Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
Quelle: aws.amazon.com

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Amazon SageMaker HyperPod now supports data capture for inference workloads

Amazon SageMaker HyperPod now supports data capture for inference workloads, enabling customers to record inference request and response payloads for model monitoring, compliance, debugging, and offline analysis. Organizations deploying generative AI and machine learning models on HyperPod need systematic visibility into the inputs flowing into their models and the outputs returned to clients to detect model drift, satisfy regulatory audit requirements, debug production issues, and build ground-truth datasets for fine-tuning. Previously, customers had to either accept limited operational visibility into their inference workloads or build expensive custom logging pipelines outside the HyperPod Inference Operator. With data capture, you can choose to record inference traffic at the SageMaker endpoint, at the load balancer, or at the model pod, depending on the level of visibility you need, and combine these options for layered observability. Captured data is delivered asynchronously to your Amazon S3 bucket and supports configurable sampling and encryption with customer-managed AWS KMS keys, so you can balance coverage with cost while keeping sensitive data protected. Data capture is designed to never block inference, ensuring production availability is preserved. You can enable data capture by configuring it on your inference endpoint when deploying models through the HyperPod Inference Operator or with SageMaker JumpStart. This feature is available for SageMaker HyperPod clusters using the EKS orchestrator in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more, see Data capture for inference on HyperPod.
Quelle: aws.amazon.com