Kategorie: Amazon AWS

Amazon Bedrock AgentCore Identity now supports On-Behalf-Of (OBO) token exchange

Amazon Bedrock AgentCore Identity now supports On-Behalf-Of (OBO) token exchange, enabling developers to build agents that securely access protected resources on behalf of authenticated users — without requiring users to complete multiple consent flows. Previously, developers building agents that needed to act on behalf of a user had to manage separate consent flows for each protected resource, adding friction for end users and complexity for builders. With OBO token exchange, developers can exchange an access token for a new scoped-down access token that carries both the original user identity and the agent identity. This token is targeted specifically to the outbound protected resource, granting just-in-time, least-privilege access without prompting the user for additional consent. Amazon Bedrock AgentCore Identity OBO token exchange is now generally available in 14 AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm). To learn more, visit the Amazon Bedrock AgentCore Identity documentation .
Quelle: aws.amazon.com

Amazon ECS Managed Instances now supports NVIDIA GPU metrics

Amazon Elastic Container Service (Amazon ECS) now offers NVIDIA GPU metrics for containerized workloads running on Amazon ECS Managed Instances. These metrics are available through Amazon CloudWatch Container Insights with enhanced observability, giving customers visibility into GPU health and performance to help troubleshoot and optimize GPU-accelerated workloads on Amazon ECS. With the new GPU metrics, Amazon ECS Managed Instances customers can now monitor GPU capacity, utilization, memory, hardware health, and thermal conditions directly in CloudWatch. Using Container Insights with enhanced observability, customers get granular visibility into these metrics, including at the GPU device level. These metrics give customers visibility into GPU operational and hardware health across their Amazon ECS Managed Instances fleet, enabling them to right-size GPU capacity, troubleshoot performance issues, and detect problems before they impact GPU-accelerated workloads, such as AI/ML training and inference. NVIDIA GPU metrics for Amazon ECS Managed Instances are available through Container Insights in all commercial AWS Regions. To get started, enable Container Insights with enhanced observability on your Amazon ECS cluster, and launch GPU-accelerated Amazon EC2 instance types through an Amazon ECS Managed Instances capacity provider. For Container Insights pricing, see Amazon CloudWatch Pricing. To learn more, see the Amazon ECS Container Insights with enhanced observability metrics user guide. 
Quelle: aws.amazon.com

AWS Outposts racks now support LagStatus CloudWatch metric

AWS Outposts racks now support the LagStatus Amazon CloudWatch metric in all AWS commercial Regions and the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions.
This metric provides you with the ability to monitor Outposts LAG connectivity status directly within the CloudWatch console, without having to rely on external networking tools or coordination with other teams. You can use this metric to set alarms, troubleshoot connectivity issues, and ensure your Outposts racks are properly integrated with your on-premises infrastructure. The LagStatus metric indicates whether an Outposts LAG is operationally up and ready to forward traffic. A value of “1” means that the LAG is up, while “0” means that it is down. When combined with the existing VifConnectionStatus and VifBgpSessionState metrics, you can quickly identify whether issues stem from LAG configuration, BGP peering, or connection problems.
The LagStatus metric is now available for all Outposts LAGs in all commercial AWS Regions and the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions where Outposts racks are available.
To get started, read this blog post and access the metrics in the CloudWatch console. To learn more, check out the CloudWatch metrics for AWS Outposts documentation for second-generation Outposts racks and first-generation Outposts racks.
Quelle: aws.amazon.com

Amazon OpenSearch Service now supports index-level encryption

Amazon OpenSearch Service now supports index-level encryption, enabling you to encrypt data at rest on a per-index basis using AWS Key Management Service (KMS) customer managed keys. You can use different customer managed keys for different indexes on the same domain, enabling more granular, tenant-specific encryption policies.
Index-level encryption builds on the existing encryption at rest capability in Amazon OpenSearch Service. While domain-level encryption uses a single AWS KMS key to encrypt all data on a domain, index-level encryption lets you specify a customer managed key for each index, isolating encrypted data across indexes. To get started, register your KMS key using the Amazon OpenSearch Service API, then specify the key ARN in the index settings when creating an encrypted index.
Index-level encryption is available at no additional cost for Amazon OpenSearch Service domains running OpenSearch version 3.3 or later. This feature is available in 14 AWS Regions: US West (Oregon), US East (Ohio), US East (N. Virginia), South America (São Paulo), Europe (Paris), Europe (London), Europe (Ireland), Europe (Frankfurt), Canada (Central), Asia Pacific (Tokyo), Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Seoul), and Asia Pacific (Mumbai).
To learn more, see Index-level encryption in the Amazon OpenSearch Service Developer Guide.
Quelle: aws.amazon.com

AWS Lambda adds support for Ruby 4.0

AWS Lambda now supports creating serverless applications using Ruby 4.0. Developers can use Ruby 4.0 as both a managed runtime and a container base image, and AWS will automatically apply updates to the managed runtime and base image as they become available. Ruby 4.0 is the latest long-term support (LTS) release of Ruby and is expected to be supported for security and bug fixes until March 2029. In addition to providing access to the latest Ruby language features, the Lambda Runtime for Ruby 4.0 also adds support for Lambda advanced logging controls, providing customers with JSON structured logs, configurable logging levels, and the ability to configure the target Amazon CloudWatch log group. The Ruby 4.0 runtime is available in all AWS Regions, including China Regions and the AWS GovCloud (US) Regions. You can use the full range of AWS deployment tools, including the Lambda console, AWS CLI, AWS Serverless Application Model (AWS SAM), CDK, and AWS CloudFormation to deploy and manage serverless applications written in Ruby 4.0. For more information on using Ruby 4.0 in Lambda, see our documentation. For more information about AWS Lambda, visit our product page. 
Quelle: aws.amazon.com

Amazon MQ for RabbitMQ now supports Prometheus metrics

Amazon MQ for RabbitMQ now supports the Prometheus plugin on RabbitMQ 4.2 brokers, providing a native Prometheus-compatible metrics endpoint on your RabbitMQ brokers. You can scrape broker, queue, and connection metrics directly from your brokers using any Prometheus-compatible monitoring tool, giving you more flexibility in how you observe and alert on your messaging infrastructure. The plugin exposes metrics through the /metrics, /metrics/detailed, and /metrics/memory-breakdown endpoints in Prometheus text format. Amazon MQ also publishes a curated subset of these Prometheus metrics to CloudWatch. With the Prometheus plugin, you can now integrate your brokers into existing Prometheus-based monitoring stacks including Grafana dashboards, Amazon Managed Service for Prometheus, and self-hosted Prometheus servers. The Prometheus plugin is enabled by default on all Amazon MQ for RabbitMQ 4.2 brokers in all AWS Regions where Amazon MQ is available. To learn more about monitoring with Prometheus, see the Amazon MQ release notes.
Quelle: aws.amazon.com

Amazon RDS for MySQL announces Innovation Release 9.6 in Amazon RDS Database Preview Environment

Amazon RDS for MySQL now supports community MySQL Innovation Release 9.6 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.6 in the Amazon RDS Database Preview Environment which provides the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases. MySQL 9.6 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next innovation minor, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.6 release notes and Amazon RDS MySQL release notes for more details. Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots created in the Preview Environment can only be used to create or restore database instances within the Preview Environment. Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region. For further information, see Working with the Database Preview Environment. To get started with the Preview Environment from the RDS console, navigate here.
Quelle: aws.amazon.com

Amazon CloudWatch adds visual agent configuration to the EC2 console

Amazon CloudWatch now provides a visual configuration editor for the CloudWatch agent directly in the Amazon EC2 console, enabling you to set up and manage observability for your EC2 instances without hand-editing JSON. The CloudWatch agent collects infrastructure and application metrics, logs, and traces from EC2 instances and sends them to CloudWatch and AWS X-Ray. With the new visual editor, you can build agent configurations graphically, selecting metrics, log sources, and deployment targets, and deploy with a single click.
From the EC2 console, you can select one or more instances, install the CloudWatch agent, or create tag-based policies for automated fleet-wide management. From the instance detail page, you can view agent status, update configurations, and troubleshoot agent health. Automated policies automatically apply the correct monitoring settings to every new instance, including those launched by auto-scaling.
To get started, navigate to the Amazon EC2 console, select an instance, and choose the EC2 monitoring tab to access the CloudWatch agent management experience. CloudWatch in-console agent management is available in all AWS Commercial Regions at no additional cost. Standard CloudWatch pricing applies for metrics, logs, and other telemetry collected by the agent.
Quelle: aws.amazon.com

Paraphrase-multilingual-MiniLM-L12-v2, Table Transformer Detection, and Bielik-11B-v3.0-Instruct are now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of paraphrase-multilingual-MiniLM-L12-v2, Microsoft Table Transformer Detection, and Bielik-11B-v3.0-Instruct in Amazon SageMaker JumpStart.
Paraphrase-multilingual-MiniLM-L12-v2 from Sentence Transformers is a lightweight semantic similarity model that maps sentences and paragraphs to a 384-dimensional dense vector space across 50+ languages. It is well suited for finding semantically similar content within and across languages, making it ideal for cross-lingual semantic search, multilingual document clustering, and sentence similarity scoring without requiring language-specific configuration.
Microsoft Table Transformer Detection is a DETR-based object detection model trained on the PubTables-1M dataset, purpose-built for detecting tables in unstructured documents such as PDFs and scanned images. It is well suited for document digitization pipelines and automated data extraction workflows that require reliably locating tabular content at scale across research papers, financial reports, and other document types.
Bielik-11B-v3.0-Instruct is an 11-billion-parameter generative language model developed by SpeakLeash and ACK Cyfronet AGH, trained on multilingual corpora spanning 32 European languages with a strong emphasis on Polish. It excels at Polish and European language dialogue, STEM and mathematical reasoning, logic and tool-use tasks, and enterprise applications requiring deep linguistic understanding across European languages.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Gemma 4 models are now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of Gemma 4 E4B, Gemma 4 26B-A4B, and Gemma 4 31B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three instruction-tuned models from Google DeepMind bring multimodal capabilities with configurable reasoning, native function calling, and multilingual support across 140+ languages, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure.
All three models share a common set of capabilities that address a broad range of enterprise AI use cases:
Thinking – Built-in reasoning mode that lets the model think step-by-step before answering
Image Understanding – Object detection, document and PDF parsing, screen and UI understanding, chart comprehension, OCR including multilingual, and handwriting recognition
Video Understanding – Analyze video content by processing sequences of frames
Interleaved Multimodal Input – Freely mix text and images in any order within a single prompt
Function Calling – Native support for structured tool use, enabling agentic workflows
Coding – Code generation, completion, and correction
Multilingual – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages
Customers can choose the model that best fits their workload: Gemma 4 E4B additionally supports audio input for automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com