Monat: August 2026

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.
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. 
Quelle: aws.amazon.com

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.
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.
Quelle: aws.amazon.com

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. 
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.
Quelle: aws.amazon.com

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).
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.
Quelle: aws.amazon.com