Kategorie: Amazon AWS

AWS Transform introduces the agent builder toolkit Kiro power for building customized transformation agents

Today, as part of the AWS Transform composability initiative, AWS announces the general availability of the agent builder toolkit Kiro power for AWS Transform. With the agent builder toolkit, AWS Partners and customers can build agents tailored to their specific modernization needs and ensure it works seamlessly within AWS Transform.
This capability enables Migration and Modernization Competency Partners, ISVs, or customers to create differentiated transformation solutions by integrating their specialized agents, tools, knowledge bases, and workflows with AWS Transform’s agentic AI capabilities. The agent builder toolkit provides the end-to-end lifecycle for transformation agents: build agents using the Kiro power; share them with teams or across partner networks, and register them with AWS Transform for discovery. The agent builder toolkit for AWS Transform is available in the Kiro power marketplace. To learn more, see AWS Transform (https://aws.amazon.com/transform).
Quelle: aws.amazon.com

SageMaker AI now supports serverless model customization for Qwen3.6

Amazon SageMaker AI now supports serverless model customization for Qwen3.6 27B parameter model using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Qwen3.6 is a popular open-weight model family from Alibaba Cloud. This launch is an addition to our support for fine-tuning Qwen3.5 and other popular models. Before this launch, you could deploy Qwen3.6 base model on SageMaker AI and now, you can also adapt it to your specific domains and workflows. Model customization enables you to tailor foundation models with your proprietary data so they more accurately reflect your domain knowledge, terminology, and quality standards. Rather than building models from scratch, fine-tuning lets you start from a capable base model and specialize it for your use cases, whether that’s improving accuracy on domain-specific tasks, aligning outputs with your organization’s tone, or improving performance on new tasks using your labeled data. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use. Serverless model customization for Qwen3.6 on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access. To learn more, see the Amazon SageMaker AI model customization documentation.
Quelle: aws.amazon.com

Amazon RDS for Oracle now supports M8i and R8i instances with Oracle SE2 License Included

Amazon RDS for Oracle now offers M8i and R8i instances with Oracle Database Standard Edition 2 (SE2) with the License Included (LI). M8i and R8i instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The new instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. With RDS for Oracle SE2 LI, customers don’t have to separately purchase Oracle license and support. Amazon RDS for Oracle SE2 LI offers subscription based pay-per-use pricing inclusive of software license, support, compute resources, and a managed database service. To use RDS for Oracle SE2 LI, customers can create database instances from the AWS Management Console or using the AWS CLI. and specify the LI option. For more details about how you can lower cost and simplify operations of running Oracle databases, refer to the AWS blog Rethink Oracle Standard Edition Two on Amazon RDS for Oracle. Configuration details for available instance types can be found on the Amazon RDS for Oracle Instance Types page. Review the AWS blog Rethink Oracle Standard Edition Two on Amazon RDS for Oracle to explore how you can lower cost and simplify operations by using Amazon RDS Oracle SE2 License Included instances for your Oracle databases. For pricing and AWS Region availability, see Amazon RDS for Oracle Pricing.
Quelle: aws.amazon.com

Amazon FSx for OpenZFS now supports creating Multi-AZ file systems in shared VPCs

Amazon FSx for OpenZFS now allows you to create Multi-AZ file systems in shared VPCs within your AWS organization, making it easier for you to decentralize network and storage administration.
VPC sharing is a feature that allows resource owners (“owner accounts”) to share one or more VPC subnets with other accounts (“participant accounts”) in their AWS organization. Participant accounts can then view, create, modify, delete, and manage their application resources in the subnets shared with them. Previously, participant accounts could create Single-AZ OpenZFS file systems in VPCs shared with them, but could only create Multi-AZ file systems in VPCs they owned. Starting today, participant accounts can create any FSx for OpenZFS file system in a shared VPC, allowing organizations to run highly available file systems with centralized network management.
You can create Multi-AZ FSx for OpenZFS file systems from shared VPC participant accounts in all AWS Regions where Amazon FSx for OpenZFS is available. To learn more, visit the FSx for OpenZFS documentation and the FSx for OpenZFS product page.
Quelle: aws.amazon.com

Amazon SageMaker Data Agent now available for IAM Identity Center domains

Amazon SageMaker Data Agent is now available in SageMaker Unified Studio domains configured with IAM Identity Center. Data Agent extends its AI-powered capabilities to help data analysts and engineers streamline their analytics workflows across both SageMaker notebooks and Query Editor environments, eliminating the need to manually write complex SQL joins, aggregations, and Python code. With Data Agent, you can describe your analysis goals in plain English and receive working Python or SQL code tailored to your connected data sources, including Amazon Athena, Amazon Redshift, Amazon S3, and AWS Glue Data Catalog. The agent maintains conversational context across notebook cells, selected tables, and query history, proposing step-by-step plans before generating code. Use it to calculate quarterly revenue growth rates, generate visualizations, transform DataFrames, or optimize query performance—all through natural language interaction. The “Fix with AI” feature provides intelligent debugging by analyzing execution errors and suggesting corrections, accelerating your development cycle. This capability is available in all commercial AWS Regions where Amazon SageMaker Unified Studio is supported. To get started, navigate to a project in SageMaker Unified Studio, open a notebook or Query Editor, and select the Data Agent panel. To learn more, visit the Amazon SageMaker Unified Studio page and refer to “Use the SageMaker Data Agent” in the Amazon SageMaker Unified Studio User Guide.
Quelle: aws.amazon.com

AWS Security Agent now supports full repository code reviews

Today, AWS announces the release of full repository code review, a new capability in AWS Security Agent that performs deep, context-aware security analysis of your entire codebase. Unlike traditional static analysis tools that match code against known vulnerability patterns, full repository code review reasons about your application’s architecture, trust boundaries, and data flows to surface systemic vulnerabilities that pattern-matching tools miss. When vulnerabilities are found, the scanner generates code remediation, specific fixes tied to the exact file and line, so teams can identify and remediate security vulnerabilities faster than ever before. This capability is available at no additional charge for existing AWS Security Agent customers during the preview.
AI-driven cybersecurity capabilities are advancing rapidly. AWS Security Agent can find vulnerabilities and build working exploits at a scale and speed we haven’t seen before. AWS is prioritizing free early access for customers, giving defenders the opportunity to strengthen their codebases and share what they learn so the whole industry can benefit.
Full repository code review is available in in all AWS Regions where AWS Security Agent is available.
To get started, visit the AWS Security Agent console to enable full repository code review and run your first review. To learn more, see the AWS Security Agent documentation.
Quelle: aws.amazon.com

AWS Lambda supports scheduled scaling for functions on Lambda Managed Instances

AWS Lambda now supports scheduled scaling for functions running on Lambda Managed Instances, using Amazon EventBridge Scheduler. This capability allows you to define one-time or recurring schedules that proactively adjust your function’s capacity limits ahead of expected traffic, to meet your performance targets during peak periods and avoid costs during idle periods. Lambda Managed Instances lets you run Lambda functions on managed Amazon EC2 instances with built-in routing, load balancing, and autoscaling. Capacity scales between your configured minimum and maximum execution environment limits based on traffic. Previously, customers with predictable traffic patterns, such as business-hours applications or marketing events, were required to manually adjust capacity limits ahead of known demand changes or build custom automation to manage scaling on a schedule. With scheduled scaling, you can now define schedules that proactively adjust your function’s capacity limits ahead of expected traffic. For example, you can schedule capacity limits to increase before business hours so execution environments are ready when the first requests arrive. You can also define a schedule that scales capacity to zero during idle periods (so you only pay when the function is actively serving traffic), and schedule it to scale back up before traffic returns. Scheduled scaling for functions running on Lambda Managed Instances is available in all AWS Regions where Lambda Managed Instances is supported. You can create schedules using the Amazon EventBridge Scheduler console, AWS CLI, AWS SDK, AWS CDK, or AWS CloudFormation. To learn more, visit the AWS Lambda Managed Instances documentation, Amazon EventBridge Scheduler documentation, AWS Lambda pricing, and Amazon EventBridge pricing.
Quelle: aws.amazon.com

Amazon Redshift launches RG instances powered by AWS Graviton

Amazon Redshift announces the general availability of RG instances, a new generation of provisioned cluster nodes powered by AWS Graviton processors that 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. This eliminates the need for Redshift Spectrum’s separate scanning fleet and its associated per-terabyte charges. Whether you’re running structured data warehouse workloads on Redshift Managed Storage or querying open-format data lake tables in Amazon S3, RG instances deliver significant performance improvements — up to 2.2x as fast as RA3 instances for data warehouse workloads, up to 2.4x as fast for Apache Iceberg queries, and up to 1.5x as fast for Parquet workloads. The natively built data lake engine features a purpose-built I/O subsystem with smart prefetch, NVMe caching, vectorized Parquet scans, and advanced file and partition-level pruning. Just-in-Time (JIT) Analyze delivers consistently fast queries without manual tuning — automatically collecting and updating table statistics as your data and workload patterns evolve. Intelligent NVMe caching keeps frequently accessed datasets close to compute, reducing round-trips to your data lake for faster response times on repeated queries. RG instances are available at launch in two instance sizes — rg.xlarge and rg.4xlarge. Existing RA3 clusters can migrate 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 No Upfront payment. For pricing details, visit the Amazon Redshift pricing page.
Amazon Redshift RG instances are now available 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 (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Europe (Milan), Europe (Spain), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Mumbai), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), Asia Pacific (Osaka), Asia Pacific (Malaysia), Asia Pacific (Hyderabad), Asia Pacific (Taiwan), and Asia Pacific (Melbourne).
To get started, refer to the following resources:

Amazon Redshift RG Instance Documentation
RA3 to RG Upgrade Guide
Amazon Redshift Pricing

Quelle: aws.amazon.com

Karpenter now supports Amazon Application Recovery Controller zonal shift

Amazon Elastic Kubernetes Service (Amazon EKS) now supports Amazon Application Recovery Controller (ARC) zonal shift and zonal autoshift when using the open source Karpenter project for compute provisioning. ARC helps you manage and coordinate recovery for your applications across AWS Regions and Availability Zones (AZs). With this launch, you can better maintain Kubernetes application availability by automating the process of shifting in-cluster network traffic away from an impaired AZ. Customers increasingly deploy highly available applications in Amazon EKS across multiple AZs to eliminate a single point of failure. With ARC zonal shift, you can temporarily mitigate an AZ impairment by redirecting in-cluster network traffic away from the impacted AZ. For a fully automated experience, authorize AWS to manage this on your behalf using ARC zonal autoshift, which includes practice runs to verify your cluster functions as expected with one less AZ. When a zonal shift is activated for your EKS cluster, Karpenter stops provisioning new capacity in the impaired AZ, halts voluntary disruptions such as consolidation and drift for nodes in that AZ, and prevents voluntary disruptions in healthy zones if they depend on scheduling pods to the impaired zone. Pods with strict scheduling requirements such as volume affinities that require the impaired zone will not trigger launch attempts. When the zonal shift expires or is canceled, Karpenter resumes normal operations. This Karpenter feature works with both manual zonal shifts and zonal autoshifts. No custom ARC resources are required as Karpenter integrates directly with the existing EKS cluster ARC resource. To enable zonal shift support, set the ENABLE_ZONAL_SHIFT setting in your Karpenter settings. To learn more, visit the Karpenter documentation and the ARC zonal shift documentation.
Quelle: aws.amazon.com

Amazon SageMaker Feature Store now supports SageMaker Python SDK V3

Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, including new capabilities for Lake Formation access controls and Apache Iceberg table properties configuration. Feature Store is a fully managed repository to store, share, and manage features for machine learning models. Data scientists can now use the modern, modular SDK v3 interfaces to manage feature groups with fine-grained access control and optimized offline storage. Data scientists can use the SageMaker Python SDK v3 to manage feature groups with streamlined workflows and reduced boilerplate. With Lake Formation integration, data scientists can enforce column-level and row-level access control on offline store data through an opt-in setting at feature group creation. With Iceberg properties support, data scientists can configure additional table properties such as compaction and snapshot expiration directly through the SDK to optimize storage and query performance. These capabilities allow data scientists to govern access to feature data and optimize offline store performance from a single SDK without managing separate tools. These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. To get started, install SageMaker Python SDK v3.8.0 or later. For more information, see Lake Formation access controls and Iceberg metadata management documentation.
Quelle: aws.amazon.com