Kategorie: Amazon AWS

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

Amazon EKS Capability for Argo CD now supports custom configuration

The Amazon Elastic Kubernetes Service (Amazon EKS) Capability for Argo CD now supports custom configuration through a standard argocd-cm ConfigMap in your cluster. This capability gives you a fully managed GitOps continuous delivery experience, and you can now tune it to fit how your teams work. You can define custom health checks for your Custom Resources, customize the Argo CD UI banner content, adjust how the capability watches and compares the resources it manages, and more. You configure these settings the same way you do in upstream Argo CD, and AWS applies them to your managed capability.
With this launch, cluster administrators now have more control over how Argo CD reports application health. By default, Argo CD has no built-in health logic for Custom Resources, so an Application can report as healthy while its resources are still provisioning, and sync waves can advance before those resources are ready. With a custom health check, you define this logic yourself. For example, a health check for a database resource can hold an Application at progressing until the database is ready. The capability also includes built-in health checks for AWS Controllers for Kubernetes (ACK) and kro (Kube Resource Orchestrator) resources, so these report accurate health with no additional configuration.
You can configure the EKS Capability for Argo CD in all AWS Regions where the capability is available. To learn more, see Amazon EKS and Configure Argo CD settings in the Amazon EKS User Guide.
 
Quelle: aws.amazon.com

Amazon Connect Customer now lets managers chat with their data

Amazon Connect Customer now lets managers chat with their data in plain language and get back the answer, the evidence behind it, and the fix, in seconds. Managers have always had the data. What they haven’t had is the time to dig through dashboards, find what’s driving performance, and decide what to do next. Now Amazon Connect Customer does that work for them. It searches across more than 150 metrics spanning self-service, agent performance, and queue performance to find what matters, explain why, and recommend the best next step.    Managers can start broad and go deep in the same conversation. For example, a manager can ask which queues are the best candidates for automation, and Amazon Connect Customer reviews where handle time and after-contact work run highest, then returns a prioritized list with confidence scores and projected impact. What once required analysts, dashboards, and weeks of investigation now becomes a prioritized action plan in seconds.    This feature is available in all AWS Regions where Amazon Connect Customer AI Agents are supported. To learn more, visit our product documentation. 
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Amazon Bedrock announces reduced pricing for OpenAI GPT-5.6 Sol

Today, OpenAI announced that they are lowering API prices for GPT-5.6 Sol. Following the recent Terra and Luna price reductions, Sol now costs $4 per million input tokens and $20 per million output tokens—20% lower input pricing and 33.3% lower output pricing. This promotional pricing is available at least through November 21, 2026.
Whether you’re building autonomous coding agents, running complex multi-step analyses, or performing advanced research workflows, the reduced pricing gives you more room to experiment and scale what’s already working. GPT-5.6 Sol delivers state-of-the-art results on agentic coding benchmarks, and the lower price point makes it more accessible for sustained, high-volume workloads.
For latest Regional availability of GPT-5.6 Sol, check the AWS Regions page. To learn more and view the pricing visit the Amazon Bedrock documentation on GPT-5.6 Sol.
Quelle: aws.amazon.com

Amazon EC2 C8gd, M8gd and R8gd instances are now available in additional AWS Regions

Amazon Elastic Compute Cloud (Amazon EC2) C8gd, M8gd, and R8gd instances with up to 11.4 TB of local NVMe-based SSD block-level storage are now available in additional regions. C8gd instances are now available in Asia Pacific (Singapore), M8gd instances are available in Mexico (Central) and Asia Pacific (Melbourne), and R8gd instances are available in Europe (Zurich). These instances are powered by AWS Graviton4 processors, delivering up to 30% better performance over Graviton3-based instances. They have up to 40% higher performance for I/O intensive database workloads, and up to 20% faster query results for I/O intensive real-time data analytics than comparable AWS Graviton3-based instances. These instances are built on the AWS Nitro System and are a great fit for applications that need access to high-speed, low latency local storage. Each instance is available in 12 different sizes. They provide up to 50 Gbps of network bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). Additionally, customers can now adjust the network and Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.
C8gd instances are ideal for compute-intensive workloads such as high-performance web servers, batch processing, distributed analytics, ad serving, video encoding, and gaming servers. M8gd instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. R8gd instances are ideal for memory-intensive workloads such as in-memory databases, real-time big data analytics, large in-memory caches, and scientific computing applications.
To learn more, see Amazon C8gd Instances, Amazon M8gd Instances and Amazon R8gd Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
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Amazon SES now supports open and click tracking override parameters

Amazon Simple Email Service (SES) now supports open and click tracking override parameters in the SendEmail and SendBulkEmail APIs. Senders can enable or disable open tracking and click tracking on an individual API call, rather than managing tracking preferences through separate configuration sets. Previously, controlling tracking behavior required maintaining a distinct configuration set for each combination of open- and click-tracking settings. With this new capability, you specify the tracking preference directly in the send request, reducing configuration overhead and simplifying how you honor recipient-level tracking consent. This is useful for senders that must respect per-recipient consent choices to meet data protection requirements such as GDPR and CNIL guidance. The tracking overrides apply per request and take precedence over the tracking behavior defined in the associated configuration set, giving you fine-grained control without changing your existing configuration set structure. There is no additional cost to use this feature. This capability is available in all AWS Regions where Amazon SES is available.
To learn more, see the documentation on open and click tracking in the Amazon SES Developer Guide. 
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AWS Glue 6.0 delivers 30% price reduction and Iceberg v3 support

AWS Glue 6.0 is now generally available, delivering a 30% price reduction and introducing full support for Apache Iceberg v3, newer versions of Apache Hudi and Delta Lake, and new capabilities to improve developer productivity. AWS Glue 6.0 also upgrades runtime to Apache Spark 4.1, Python 3.13, and Scala 2.13.
With Apache Iceberg v3, AWS Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution through UNKNOWN data type and DEFAULT column values. Glue 6.0 also introduces features that boost developer productivity and performance, such as Spark Declarative Pipelines that eliminate repetitive orchestration code, Real-Time Mode streaming for sub-second latencies, and Arrow-native Python UDFs for improved PySpark performance. These capabilities help you implement large-scale ETL, recurring batch workloads, streaming analytics, and AI application development using AWS Glue.
AWS Glue 6.0 is available in all AWS Commercial, AWS GovCloud (US), and AWS China regions.
To get started, select Glue 6.0 from the version dropdown in the AWS Glue console or SageMaker Unified Studio when creating a new job, or migrate existing jobs using the Spark Upgrade Agent. To learn more, visit the AWS Glue documentation and AWS Glue pricing.
Quelle: aws.amazon.com