Kategorie: Amazon AWS

AWS DevOps Agent adds support for bidirectional Slack communication

AWS DevOps Agent now enables engineers to manage production operations across AWS, multicloud, and on-premises environments with the complete investigation lifecycle directly within Slack. Previously, on-call engineers and DevOps teams faced fragmented workflows during high-severity incidents, having to context-switch between communication and investigation platforms. This update consolidates both surfaces into a single, unified location where incident response already happens.
Engineering teams can now initiate and steer investigations by simply @mentioning AWS DevOps Agent in any connected Slack private channel. All investigation activity, including team-contributed context, agent findings, and recommended actions, is captured within a single thread. Ask questions about AWS resources, system metrics, alarm status, deployment history, and incident patterns, reducing cognitive load during critical moments and making production operations significantly more efficient.
This capability is available in all commercial AWS Regions where AWS DevOps Agent is currently supported.
To get started, read the user guide. See all the latest AWS DevOps Agent features on the release history page.
Quelle: aws.amazon.com

Amazon Bedrock Managed Knowledge Base now supports multimodal embeddings for video, audio, and image content with TwelveLabs Marengo 3.0

AWS announces the availability of TwelveLabs Marengo 3.0 as an embedding model in Amazon Bedrock Managed Knowledge Base, enabling customers to create multimodal embeddings for video, audio, and image content. Amazon Bedrock Managed Knowledge Base already supports media search by transcribing audio and video to text and generating text-based embeddings—Marengo 3.0 goes further by encoding visual scenes, speech, and video cues directly into multimodal embeddings, capturing meaning that transcription alone cannot. Simply upload your media assets from data sources such as Amazon S3, sync, and search using natural language—with no infrastructure to manage. Marengo 3.0 produces compact 512-dimensional vectors, delivering state-of-the-art retrieval accuracy. Results include segment start and end times, enabling applications to jump directly to the relevant moment in a video. This unlocks use cases across sports analytics, media and entertainment, security, education, and retail—from finding specific plays across seasons of game footage to locating lecture segments by concept rather than keywords. The model offers configurable segmentation options to match your content structure. To learn more, see TwelveLabs Marengo 3.0 embedding model integration in the Amazon Bedrock Knowledge Base User Guide. For more information, visit the Amazon Bedrock Knowledge Bases product page.
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now supports model caching for faster inference autoscaling and reduced cold starts

Amazon SageMaker HyperPod now supports model caching, an inference optimization that pre-loads model weights and container images onto cluster nodes so pods start in seconds instead of minutes.
When running LLM inference at scale for workloads like chat assistants, agentic pipelines, RAG, and document analysis, cold start is a real bottleneck. Deployments and scale-out events spend most of their time downloading container images and model weights. As model size increases, this gets worse, with large models taking tens of minutes before they can serve traffic.
Model caching solves this with two independent capabilities. The weights cache stores model weights on local NVMe so pods read from fast local storage instead of pulling from S3 or FSx over the network. The image cache pre-pulls the container image so pods skip the ECR download entirely. If a pod lands on a node without a warm cache, it falls back to pulling from the original source automatically, so there is no risk of pods getting stuck or failing.
Benchmarks across models from 57 GB to 145 GB show around 60% faster scale-out, and the image cache cuts over two minutes of image-pull time (97% reduction). The benefit grows with model size while retaining the reliability of the original source path.
Customers enable model caching through the HyperPod Inference Operator by adding a modelCacheConfig section to their InferenceEndpointConfig or JumpStartModel resource. The operator handles the full lifecycle with no manual setup or cleanup.
Model caching is now generally available in all regions where SageMaker HyperPod is available. To get started, see the SageMaker HyperPod documentation.
 
Quelle: aws.amazon.com

Amazon EC2 X2idn instances are now available in Asia Pacific (Hong Kong)

Memory-optimized Amazon Elastic Compute Cloud (Amazon EC2) X2idn instances are now available in Asia Pacific (Hong Kong) Region. These instances, powered by 3rd generation Intel Xeon Scalable Processors and built with AWS Nitro System, are designed for memory-intensive workloads and deliver improvements in performance compared to previous generation X1 instances. These instances are SAP-certified for running Business Suite on HANA, SAP S/4HANA, Data Mart Solutions on HANA, Business Warehouse on HANA, SAP BW/4HANA, and SAP NetWeaver workloads on any database.
Quelle: aws.amazon.com

AWS Lambda recursive loop detection is now available in Europe Sovereign Cloud

AWS Lambda recursive loop detection is now supported for functions running in Europe Sovereign Cloud. Recursive loop detection automatically detects and stops recursive invocations between Lambda functions and other supported services, preventing unexpected billing caused by unintended recursive loops.
Customers use event sources such as Amazon S3, Amazon SQS, and Amazon SNS to build event driven applications that trigger Lambda functions. Misconfiguration or code defect can cause events to be sent back to the same source that triggered the Lambda function, causing recursive loops and unintended usage. When such a loop is detected, recursive loop detection automatically stops processing the event and sends you an AWS Health Dashboard notification with troubleshooting steps.
Recursive loop detection is enabled by default for Lambda functions using a supported SDK version. If your function intentionally uses recursive loops, you can use the PutFunctionRecursionConfig API to turn off recursive loop detection on your Lambda function. 
To learn about recursive loop detection, visit Lambda documentation.
Quelle: aws.amazon.com

AWS Transform for .NET now generates unit tests for modernized code

Today, AWS announced that AWS Transform for .NET can automatically generate unit tests for the code it modernizes. When enabled, AWS Transform generates unit tests that target the testable classes in your transformed .NET application, such as business logic and controllers, giving you an automated test safety net on the modernized code as part of the same job that performs the migration.
Modernizing a .NET Framework application to modern .NET produces a transformed, buildable codebase, but teams previously had to write test coverage for that modernized code by hand. With this launch, AWS Transform assesses your application for testability in parallel with the standard .NET assessment, plans which classes and methods to cover, and generates the corresponding unit test code, so you complete the migration with tests already in place. Unit test generation is opt-in: you can enable it at the start of a job or after transformation completes.
Unit test generation for AWS Transform for .NET is supported in the AWS Toolkit for Visual Studio extension. It is available in all AWS Regions where AWS Transform for .NET is supported. To get started, run a .NET transformation with AWS Transform for .NET in Visual Studio and choose to generate unit tests. To learn more, refer to Modernizing .NET in the IDE in the AWS Transform User Guide.
Quelle: aws.amazon.com

Amazon API Gateway now supports 1 MB execution logs with configurable delivery destinations

Amazon API Gateway now supports configurable delivery destinations and larger log events for REST API execution logs. Previously, execution logs were delivered to a single API Gateway-managed CloudWatch Logs log group with log events truncated at 1 KB, limiting visibility into request and response data. You can now route execution logs up to 1 MB to your own Amazon CloudWatch Logs log groups, Amazon S3 buckets, or Amazon Data Firehose streams, and deliver to multiple destinations simultaneously. For example, you can route execution logs to Amazon S3 in Apache Parquet format for cost-efficient long-term storage and analysis with Amazon Athena, while simultaneously delivering structured JSON logs to CloudWatch Logs for real-time alerting. This feature is available in all AWS Regions where API Gateway REST APIs are available, including the AWS GovCloud (US) Regions. Execution logs delivered through this feature are charged at vended logs rates. For pricing details, see Amazon CloudWatch Pricing. You can set up delivery through the API Gateway console, AWS CLI, or AWS CloudFormation. To get started, see Amazon API Gateway documentation and AWS blog post. 
Quelle: aws.amazon.com

Amazon Redshift RG instances now available in Europe (Zurich) Region

Amazon Redshift RG instances, powered by AWS Graviton processors, are now available in the AWS Europe (Zurich) Region. RG instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift’s custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, enabling you to run SQL analytics across your data warehouse and data lake using a single engine.
RG instances are available in four instance sizes, rg.large, rg.xlarge, rg.4xlarge and rg.12xlarge. Customers with existing RA3 clusters can upgrade them to RG using Snapshot & Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with All Upfront, Partial Upfront, and No Upfront payment options. For pricing details, visit the Amazon Redshift pricing page. To get started, refer to the following resources:

Amazon Redshift RG Instance Documentation
RA3 to RG Upgrade Guide
Amazon Redshift pricing page

Quelle: aws.amazon.com

Amazon OpenSearch Serverless is now available on v0 by Vercel

You can now build full-stack search and AI applications in minutes using Amazon OpenSearch Serverless on v0 by Vercel, an AI-powered platform that transforms your ideas into production-ready web applications. OpenSearch Serverless eliminates infrastructure management and automatically scales capacity up and down based on demand, so you can focus on building and not managing clusters. With this launch, you can use natural language prompts to build applications powered by OpenSearch Serverless for full-text search and vector search for retrieval-augmented generation (RAG) workloads, all without leaving the v0 interface.
To get started, simply describe what you want to build using a natural language prompt in v0 or visit OpenSearch Serverless in v0 to begin with Amazon OpenSearch Serverless pre-selected. v0 generates a complete full-stack application, automatically provisions an Amazon OpenSearch Serverless collection, indexes your data into the collection, and uses the Amazon OpenSearch Serverless endpoint for serving search queries. v0 handles the required environment variables and configuration, and loads provider-specific agent skills to generate code that follows recommended patterns. You can either prompt v0 to provision Amazon OpenSearch Serverless resources under a new AWS account or link to an existing AWS account.
You can create Vercel apps with Amazon OpenSearch Serverless in v0 in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (London), Europe (Paris), Europe (Frankfurt), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and Asia Pacific (Osaka). To learn more, check out the Vercel announcement or visit the documentation for Amazon OpenSearch Serverless.
Quelle: aws.amazon.com

Announcing second-generation single-rack AWS Outposts

Today, AWS announces the general availability of second-generation single-rack AWS Outposts, a self-contained 42U rack that integrates compute, storage and networking into a single compact unit purpose-built for workloads requiring low latency, local data processing, and data residency in space and power constrained locations. A single-rack Outposts delivers up to 2,688 vCPU and 100 TB of Amazon Elastic Block Store (Amazon EBS) storage. Moreover, like multi-rack Outposts, single-rack Outposts support the latest x86-powered EC2 instances, including general purpose (M7i, M8i), compute-optimized (C7i, C8i), memory-optimized (R7i, R8i), and Outposts accelerated networking (Bmn-sf2e, Bmn-cx2, Bmn-cx3a) instances.
For organizations that operate in locations with limited rack space, such as manufacturing, gaming, and other industries, single-rack Outposts brings the latest AWS compute, storage, and networking features on-premises, and gives customers a direct path to modernize while leveraging their currently available space and power. Single-rack and multi-rack Outposts offer customers a consistent experience with the same AWS APIs, management console, automation, governance policies, and security controls across AWS Regions and on-premises locations.
For a current list of AWS Regions and countries/territories where Outposts racks are supported, check out the Outposts rack FAQs page. To get started, open the AWS Outposts console.
Quelle: aws.amazon.com