Kategorie: Amazon AWS

Amazon Connect Customer Global Resiliency now provides business users visibility into resiliency readiness across linked Regions

Amazon Connect Customer Global Resiliency now provides a Global Resiliency page in the admin website, giving administrators a single place to review their Region switch readiness. The page shows an overall readiness status and lists the resources that Global Resiliency natively replicates, including which resource types are in sync, which are still pending synchronization, and which need action. For example, if a phone number or user is not associated with a traffic distribution group, the page flags it and lets the administrator associate the affected resources directly from the page. With this readiness overview, administrators can review their resiliency posture at any time and verify that their instance is ready for a Region switch.
This feature is available in all AWS Regions where Amazon Connect Customer Global Resiliency is supported. To learn more, see Amazon Connect Customer admin guide.
Quelle: aws.amazon.com

AWS Partner Central adds Solution Matching Engine performance insights

AWS Partner Central now gives AWS Partners more visibility into how the Solution Matching Engine (SME) contributes to their co-sell pipeline. The Solution Matching Engine performance section in Partner Analytics helps partners understand how SME performs for their company and AWS Marketplace listings.
Partners can view recommendations, SME-attributed opportunities, their share of AWS-originated opportunities, stall rate, and pipeline attributed to SME. They can filter by recommendation type or AWS Marketplace listing and explore top customer profiles by Region, industry, and segment. These insights help partners assess their visibility and decide where to improve their listings and opportunity information.
All partners eligible for APN Customer Engagements (ACE) can find the section on the Opportunity pipeline analysis page under Partner Analytics in AWS Partner Central. To learn more about SME and how to strengthen your visibility with AWS sellers, read Solution Matching Engine: Your Gateway to AWS Sellers.
Quelle: aws.amazon.com

Amazon GuardDuty adds optional threat detection rules for Runtime Monitoring

Amazon GuardDuty now supports optional detection rules for Runtime Monitoring. You can enable more than 50 Runtime Monitoring rules that help detect process, file, and network activity that might be routine in one environment but a sign of compromise in another, such as modifying sudoers files or disabling audit logging.
The new rules provide detections for Amazon EC2 instances, Amazon EKS, and Amazon ECS on both EC2 and AWS Fargate, and span 13 finding types mapped to MITRE ATT&CK® tactics including persistence, privilege escalation, execution, defense evasion, discovery, exfiltration, and command and control. This release introduces four new finding types: CommandAndControl:Runtime/SuspiciousCommand, CredentialAccess:Runtime/SensitiveFileRead, Discovery:Runtime/SensitiveFileRead, and Exfiltration:Runtime/SuspiciousCommand.
To get started, visit the GuardDuty console. You can enable rules in dry-run mode to evaluate detection efficacy before enabling live detection.
Custom Detection Rules for Runtime Monitoring is available in all AWS commercial Regions and the AWS GovCloud (US) Regions. To learn more, see Amazon GuardDuty Custom Detection Rules. To receive programmatic updates on new Amazon GuardDuty features and threat detections, subscribe to the Amazon GuardDuty SNS topic.
Quelle: aws.amazon.com

EC2 Image Builder now integrates with ASG for faster image builds

EC2 Image Builder now supports using existing instances from an Auto Scaling Group (ASG) for building images, enabling faster builds by eliminating the time spent in launching and bootstrapping new instances and reusing already running EC2 instances.
With this feature, you can provide an ASG in your Image Builder infrastructure configuration. Instead of launching a new instance at the build time, Image Builder selects an already-running instance from this ASG, applies your build components, and creates the image. The ASG continuously replenishes the pool as instances are used for builds, ensuring instances are always available for subsequent pipeline runs.
This feature is fully opt-in and does not affect existing pipelines. Pipelines without an ASG configured continue to use the default launch path with no behavior change.
This feature is available in all AWS Regions at no additional cost. To configure ASG integration in your Image Builder pipelines, see the EC2 Image Builder documentation.
Quelle: aws.amazon.com

Amazon CloudWatch Omni is now available in additional AWS Regions

AWS announces that Amazon CloudWatch Omni is now available in eight additional AWS Regions. CloudWatch Omni is an AI-powered observability experience, organized around your teams and the applications they run, that helps you observe and troubleshoot your applications and agents in one place. It combines the interoperability of OpenTelemetry with the scale and reliability of CloudWatch, and meets you where you work — a standalone web experience with single sign-on (SSO), or a local IDE extension.
With CloudWatch Omni, You create spaces in your central accounts to see telemetry across your AWS accounts, Regions, and other clouds, including Azure and GCP. Omni discovers services, maps dependencies, and surfaces golden metrics automatically and you can interact with telemetry through chat, point-and-click, or your own tool via Agent Toolkit for AWS to find signals, build dynamic views, and reach root cause powered by AWS DevOps Agent. A dedicated agent observability experience adds an evaluation-driven workflow for frameworks including LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK, and Strands.
With this expansion, CloudWatch Omni is now available in Canada (Central), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (London), and South America (São Paulo), in addition to US East (N. Virginia), US West (Oregon), and Europe (Ireland).
To get started, create your Omni space from the CloudWatch console, configure SSO, and sign in. Agent developers can install the free CloudWatch Omni extension for VS Code, Cursor, and Kiro. To learn more, see the Amazon CloudWatch Omni product page and documentation. For pricing, see the CloudWatch Omni pricing page.
Quelle: aws.amazon.com

Amazon CloudWatch log centralization now available in AWS GovCloud and additional commercial Regions

Amazon CloudWatch log centralization is now available in AWS GovCloud (US) and 13 additional commercial Regions. CloudWatch log centralization copies log data from multiple AWS accounts and Regions into a single destination account, integrating with AWS Organizations for efficient aggregation without the need for custom solutions.
With this launch, organizations in the AWS GovCloud (US) partition and commercial partition can each centralize logs across their accounts and Regions for centralized monitoring. You can scope centralization rules to your entire organization, specific organizational units, or selected accounts and copy logs into a single account while preserving source context through enriched system fields.
CloudWatch log centralization is now additionally available in Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Canada West (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), AWS GovCloud (US-East), and AWS GovCloud (US-West). For a complete list of supported Regions, see the AWS Region table.
To get started, create a centralization rule in the Amazon CloudWatch console, or by using the AWS CLI or AWS SDKs. To learn more, see Log centralization in the Amazon CloudWatch Logs User Guide. Customers can centralize one copy of logs for free; additional copies are charged at $0.05/GB. For details, see Amazon CloudWatch pricing.
Quelle: aws.amazon.com

Second-generation AWS Outposts racks now supported in the AWS Mexico (Central) Region

Second-generation AWS Outposts racks are now supported in the AWS Mexico (Central) Region. Outposts racks extend AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience. 

 

Organizations of every size, from startups to enterprises to public sector agencies, can now order Outposts racks connected to this newly supported Region to optimize for latency and data residency needs. This regional expansion provides additional flexibility in the number of AWS Regions that customers’ Outposts can connect to.
To learn more about second-generation Outposts racks, read the blog post and the user guide. For the latest list of countries and territories and the AWS Regions where second-generation Outposts racks are supported, see the Outposts rack FAQs page.
Quelle: aws.amazon.com

AWS Transform announces Azure-to-AWS cloud-native code conversion

Today, AWS announced the general availability of application code conversion from Azure SDK to AWS SDK in AWS Transform, giving you an accelerated path to migrate Microsoft Azure cloud-native .NET applications to AWS. The AWS Transform .NET modernization agent identifies Azure service code and converts it to equivalent AWS service code. For example, an Azure Functions serverless function that uses Azure Cosmos DB can be converted to an AWS Lambda function that uses Amazon DynamoDB.
AWS Transform discovers the Azure SDK in your submitted source code and presents the conversion options in the assessment report and modernization plan. You can accept the options provided or adjust them based on your preferences before running the modernization. AWS Transform then converts the identified Azure service code to the equivalent AWS SDK calls, and validates that the changes are incorporated into the transformed code. The feature supports conversion of Azure Functions to AWS Lambda functions, Azure Blob Storage to Amazon S3, and Azure Queue Storage to Amazon SQS, with Azure Cosmos DB to Amazon DynamoDB conversion available in preview.
AWS Transform is available in eight AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (Frankfurt, London).
To learn more, see Convert cloud-native code to AWS in the AWS Transform User Guide.
Quelle: aws.amazon.com

Beam model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium

Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks. With it, customers can now build advanced coding agents and automated workflows on a frontier open weight model while keeping control of their data and infrastructure and adapting the model to their business. Beam runs alongside other models that customers already use in SageMaker AI and scales to meet production demand with the same controls, audit trails, and monitoring built into every SageMaker AI deployment. Additionally, Beam was ported and optimized for AWS Trainium3, giving customers a high-performance, cost-efficient option for deploying and scaling the model from day one. “Reflection is delivering frontier AI that developers can actually control, not just rent through an API. Bringing Beam to SageMaker AI puts that capability in front of millions of developers on infrastructure they already trust, running in their own AWS account, on their own data, fully in their control,” said Misha Laskin, co-founder and CEO, Reflection.
Engineering teams can use Beam to resolve software issues, generate and review code, and automate routine operations work. Businesses can also use it for complex workflows that span many steps and tools, and for advanced math and science reasoning. Because Beam is released under the Apache 2.0 license, customers can inspect and customize the model, while also running it in their own AWS accounts, so data stays private and under their control, protected by the same AWS security and compliance capabilities they already rely on.  “Advanced AI models like Beam, now available on Amazon SageMaker, let us run open weight frontier AI entirely inside our own secure environments, with full visibility into the weights, the data, and the pipeline. In the age of multi-model AI, the pairing of openness and managed infrastructure is an essential addition to the AI ecosystem. The freedom to deploy and adapt advanced AI, tailored to mission problems with client-specific security and controls, gives more agencies greater flexibility to deploy advanced AI across their enterprise.” said Garrett Berntsen, Chief AI Officer, Accenture Federal Services.
Beam is initially available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). To start, search for “Beam” in SageMaker JumpStart within Amazon SageMaker Studio, or deploy it directly on SageMaker AI.  To learn more, read the AWS/Beam launch blog, SageMaker AI documentation and SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Nemotron 3.5 Super VL model now available on Amazon SageMaker JumpStart

NVIDIA’s Nemotron 3.5 Super VL is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This vision-language model from the Nemotron 3.5 family accepts images, video, and text as input and generates text, enabling customers to deploy multimodal AI solutions on AWS infrastructure.
Nemotron 3.5 Super VL is designed for document intelligence, GUI understanding, video question answering, and multimodal agentic applications. It pairs a 120B-parameter hybrid Mixture-of-Experts (MoE) language backbone with 12B active parameters and a vision encoder, supporting context lengths of up to 1 million tokens. The model includes a built-in Multi-Token Prediction (MTP) head for speculative decoding to accelerate text generation, with reasoning configurable per request. It supports ten languages including English, French, Spanish, German, Japanese, Hindi, Korean, and Chinese. Post-trained with multimodal supervised fine-tuning on image, video, document, chart, and GUI data followed by GRPO reinforcement learning, it delivers structured JSON output, tool calling, and configurable reasoning for enterprise agent workflows. SageMaker JumpStart offers two variants: NVFP4 for optimized inference on G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, and BF16 reference weights on ml.p5en.48xlarge for customization including SFT, RL, distillation, and domain adaptation.
With SageMaker JumpStart, customers can deploy Nemotron 3.5 Super VL with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console 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