8/24/2026, 12:00:00 AM ~ 8/25/2026, 12:00:00 AM (UTC)

Recent Announcements

SageMaker MLflow now supports customer managed keys

SageMaker MLflow now enables customers to encrypt their data using customer-managed keys (CMK) through AWS Key Management Service (KMS).\n This enhancement allows organizations with strict security and compliance requirements to manage their own encryption keys. With customer-managed keys, you gain enhanced security control and comprehensive audit capabilities through AWS CloudTrail integration. You can encrypt your data with your own KMS keys, trace all data access for security auditing.

Customer-managed keys must be created in the same AWS account and region as your MLflow App, and only symmetric AWS KMS keys are supported.

This feature is generally available in all AWS Regions where MLflow App is available. To learn more, visit the SageMaker MLflow detail page.

Amazon EKS now supports multiple external OIDC identity providers per cluster

Amazon Elastic Kubernetes Service (Amazon EKS) now supports multiple external OpenID Connect (OIDC) identity providers per cluster. You can associate up to 10 OIDC identity providers with a single cluster, giving you more flexibility in how you authenticate users and workloads to your Kubernetes clusters.\n Many organizations use different identity providers for different user populations, such as employees, contractors, and CI/CD systems. You can now associate each of these providers directly with your cluster, without consolidating users into a single provider or running an intermediary identity broker. Each provider is configured and managed independently, so each population authenticates through its own provider and identity mapping. Your existing IAM authentication continues to work alongside every configured provider. You add each provider the same way as before, using the AWS Management Console or the AssociateIdentityProviderConfig API through the AWS CLI and AWS SDKs. This capability is available at no additional cost in all AWS Regions where Amazon EKS is available. To learn more, see Grant users access to Kubernetes with an external OIDC provider in the Amazon EKS User Guide.

Amazon Aurora now supports PostgreSQL 18.4, 17.10, 16.14, 15.18, and 14.23

Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL versions 18.4, 17.10, 16.14, 15.18, and 14.23 which include bug fixes from the PostgreSQL community and Aurora-specific enhancements. We recommend upgrading to the latest minor versions to address known Common Vulnerabilities and Exposures (CVEs) and benefit from these improvements, as detailed in the release notes. \n You can upgrade your databases during scheduled maintenance windows using automatic minor version upgrades. To simplify operations at scale, enable automatic minor version upgrades and use the AWS Organizations Upgrade Rollout Policy to orchestrate multiple upgrades in phases, validating on lower-priority environments before upgrading your most critical ones. For more information, see Upgrading Amazon Aurora PostgreSQL DB clusters.

Amazon Aurora is designed for high performance and availability at global scale with full PostgreSQL compatibility. It provides scale-to-zero serverless compute, Aurora Global Database for multi-Region resilience, Aurora I/O-Optimized for improved price performance on I/O-intensive workloads, and built-in security and continuous backups. To get started, take a look at our getting started page.

Amazon SageMaker HyperPod enhances support for Ray

Amazon SageMaker HyperPod now enhances support for Ray with built-in observability, resilient training, accelerated inference and managed development environments. Ray is a popular open-source framework for scaling AI workloads on a unified compute layer, from data processing and distributed training to reinforcement learning and model serving. Running Ray on Kubernetes at production scale can be an operational burden: job hangs, low GPU utilization from static team allocations, and multi-step observability setup. Also, lack of interactive development environment means every code change needs another job submission and familiarity with kubectl.\n HyperPod now brings easier development, resilient training, and accelerated inference to Ray. Data scientists create, edit, monitor, and delete Ray clusters from a web-based interface in Amazon SageMaker Studio, then attach JupyterLab, Code Editor, or a local IDE to a running Ray cluster and iterate interactively against cluster-scale compute. A multi-node Ray cluster behaves like a local development environment, so you test each change immediately, without waiting for a new job to queue and start. For Observability, HyperPod provisions Grafana dashboards with metrics in Amazon Managed Service for Prometheus and allows one-click access to the Ray Dashboard through a secure browser link, giving you visibility into your workloads from the first run. For training at scale, HyperPod node auto recovery and hung job detection handle GPU faults, job hangs, loss spikes, and degraded throughput. Tiered checkpointing restores state from cluster memory to maximize goodput, and task governance improves compute utilization through quotas, priorities, and preemption. Together, these keep your long training runs progressing through failures and maximize the useful work done per GPU-hour. For inference with Ray Serve, a tiered KV cache reuses cached prefixes to reduce time to first token, and you can deploy Amazon SageMaker JumpStart models directly.

Open-source Ray code runs unchanged and you can either adopt the purpose-built experience in SageMaker Studio or take individual capabilities to integrate into your own ML platform.

Ray support is available for HyperPod clusters orchestrated by Amazon EKS, in AWS Regions where SageMaker HyperPod is supported. To learn more, see the SageMaker HyperPod documentation, and explore the interactive demo.

Amazon Connect Customer now supports information extraction for agent voice and chat conversations

Amazon Connect Customer now supports information extraction, which automatically captures key data from voice and chat interactions, reducing manual data capture and improving agent and supervisor productivity. Information extraction captures verbatim values like account numbers, reservation IDs, and product names, as well as derived insights inferred from the conversation such as reason for contact, resolution provided, and next steps promised.\n You define conversational analytics rules for what to extract and when. Extraction operates on raw contact content before redaction, so you can capture specific data points while still redacting sensitive values from recordings and transcripts. Agents see extracted values during After Contact Work, supervisors use them to search and review contacts, and developers access them programmatically through APIs, Kinesis Data Streams, and S3 output files. You can also feed extracted values directly into rule actions like email notifications, task creation, and case creation, turning unstructured conversations into automated experiences. For example, a travel company can automatically extract “Hotel Name,” “Reservation ID,” and “Reason for call” from interactions, then populate outbound emails and create follow-up tasks, eliminating manual data entry and reducing handle time. To learn more, see Information extraction in the Amazon Connect Customer Administrator Guide, or visit the Amazon Connect Customer website. For a complete list of conversational analytics capabilities available by AWS Region, refer to Availability of Connect Customer features by Region.

AWS ParallelCluster 3.16 adds an on-node diagnostics tool

AWS ParallelCluster 3.16 is now generally available with a new on-node diagnostics tool, cluster stability improvements, and an updated HPC and AI/ML software stack.\n pcluster-diag is a diagnostics tool built into the ParallelCluster AMIs that lets you run diagnostic checks on any cluster node with a single command, and get a structured report that makes it easier to identify issues. This release also hardens the cluster lifecycle with more resilient cluster creation, updates, and image builds. The software stack is refreshed, with updated NVIDIA driver, CUDA, EFA installer, and Slurm versions. To get started with pcluster-diag, see Troubleshooting with pcluster-diag. For more details, review the AWS ParallelCluster 3.16.0 release notes.

AWS ParallelCluster is an open-source cluster management tool that makes it possible for R&D customers and IT administrators to operate high-performance computing (HPC) clusters on AWS. ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific and engineering workloads at scale on 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 ParallelCluster User Guide. To start using ParallelCluster, see the installation instructions for ParallelCluster UI and CLI.

OpenAI GPT-5.6 Terra and Luna now available on Amazon Bedrock in AWS GovCloud (US)

GPT-5.6 Terra and Luna are now generally available on Amazon Bedrock in AWS GovCloud (US-West) and AWS GovCloud (US-East), bringing the smartest family of models from OpenAI yet to Bedrock’s next-generation inference engine built for high-performance, security and reliability. GPT-5.6 sets a new standard for intelligence and efficiency, allowing you to solve harder problems in less time and with more intelligence per token. The two models span capability tiers from balanced performance (Terra) to fast, cost-efficient inference (Luna).\n With GPT-5.6, you can build autonomous coding agents, run long-horizon genomics and biology analyses, and perform advanced cybersecurity research. Terra provides GPT-5.5-level performance at half the cost and Luna brings fast, affordable inference at the lowest price point. GPT-5.6 also supports prompt caching with explicit cache breakpoints, so repeated context across agentic workflows is billed at a 90% discount and doesn’t compound cost as you scale. 

GPT-5.6 Terra and Luna support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.

For regional availability, please see the Amazon Bedrock regional availability page.  Get started with Terra and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.

Amazon RDS for MySQL now supports new minor version 8.4.11

Starting today, Amazon Relational Database Service (Amazon RDS) for MySQL supports MySQL minor version 8.4.11, the latest minor released by community MySQL. In addition to operational improvements, MySQL 8.4.11 introduces support for post-quantum TLS (PQ-TLS) key exchange, providing you with post-quantum cryptography options for encrypting your data in-transit. We recommend upgrading to the newer minor versions to accept fixes for CVEs in prior versions of MySQL and to benefit from bug fixes, performance improvements, and new functionality added by the MySQL community. Learn more about the enhancements in RDS for MySQL 8.4.11 in the Amazon RDS user guide and MySQL 8.4.11 release notes.\n You can leverage automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance windows. You can also use Amazon RDS Managed Blue/Green deployments for safer and simpler updates to your MySQL instances. Learn more about upgrading your database instances, including automatic minor version upgrades and Blue/Green Deployments, in the Amazon RDS User Guide. Amazon RDS for MySQL makes it simple to set up, operate, and scale MySQL deployments in the cloud. Learn more about pricing details and regional availability at Amazon RDS for MySQL. Create or update a fully managed Amazon RDS for MySQL database in the Amazon RDS Management Console.

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