Kategorie: Amazon AWS

Announcing TypeScript support in Strands Agents (preview) and more

In May, we open sourced the Strands Agents SDK, an open source python framework that takes a model-driven approach to building and running AI agents in just a few lines of code. Today, we’re announcing that TypeScript support is available in preview. Now, developers can choose between Python and TypeScript for building Strands Agents. TypeScript support in Strands has been designed to provide an idiomatic TypeScript experience with full type safety, async/await support, and modern JavaScript/TypeScript patterns. Strands can be easily run in client applications, in browsers, and server-side applications in runtimes like AWS Lambda and Bedrock AgentCore. Developers can also build their entire stack in Typescript using the AWS CDK. We’re also announcing three additional updates for the Strands SDK. First, edge device support for Strands Agents is generally available, extending the SDK with bidirectional streaming and additional local model providers like llama.cpp that let you run agents on small-scale devices using local models. Second, Strands steering is now available as an experimental feature, giving developers a modular prompting mechanism that provides feedback to the agent at the right moment in its lifecycle, steering agents toward a desired outcome without rigid workflows. Finally, Strands evaluations is available in preview. Evaluations gives developers the ability to systematically validate agent behavior, measure improvements, and deploy with confidence during development cycles. Head to the Strands Agents GitHub to get started building.
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now supports checkpointless training

Amazon SageMaker HyperPod now supports checkpointless training, a new foundational model training capability that mitigates the need for a checkpoint-based job-level restart for fault recovery. Checkpointless training maintains forward training momentum despite failures, reducing recovery time from hours to minutes. This represents a fundamental shift from traditional checkpoint-based recovery, where failures require pausing the entire training cluster, diagnosing issues manually, and restoring from saved checkpoints, a process that can leave expensive AI accelerators idle for hours, costing your organization wasted compute.
Checkpointless training transforms this paradigm by preserving the model training state across the distributed cluster, automatically swapping out faulty training nodes on the fly and using peer-to-peer state transfer from healthy accelerators for failure recovery. By mitigating checkpoint dependencies during recovery, checkpointless training can help your organization save on idle AI accelerator costs and accelerate time. Even at larger scales, checkpointless training on Amazon SageMaker HyperPod enables upwards of 95% training goodput on cluster sizes with thousands of AI accelerators.
Checkpointless training on SageMaker HyperPod is available in all AWS Regions where Amazon SageMaker HyperPod is currently available. You can enable checkpointless training with zero code changes using HyperPod recipes for popular publicly available models such as Llama and GPT OSS. For custom model architectures, you can integrate checkpointless training components with minimal modifications for PyTorch-based workflows, making it accessible to your teams regardless of their distributed training expertise.
To get started, visit the Amazon SageMaker HyperPod product page and see the checkpointless training GitHub page for implementation guidance.
Quelle: aws.amazon.com

Announcing new memory-optimized Amazon EC2 X8aedz Instances

AWS announces Amazon EC2 X8aedz, next generation memory optimized instances, powered by 5th Gen AMD EPYC processors (formerly code named Turin). These instances offer the highest maximum CPU frequency, 5GHz in the cloud. They deliver up to 2x higher compute performance and 31% price-performance compared to previous generation X2iezn instances. X8aedz instances are built using the latest sixth generation AWS Nitro Cards and are ideal for electronic design automation (EDA) workloads such as physical layout and physical verification jobs, and relational databases that benefit from high single-threaded processor performance and a large memory footprint. The combination of 5 GHz processors and local NVMe storage enables faster processing of memory-intensive backend EDA workloads such as floor planning, logic placement, clock tree synthesis (CTS), routing, and power/signal integrity analysis. X8aedz instances feature a 32:1 ratio of memory to vCPU and are available in 8 sizes ranging from 2 to 96 vCPUs with 64 to 3,072 GiB of memory, including two bare metal variants, and up to 8 TB of local NVMe SSD storage. X8aedz instances are now available in US West (Oregon) and Asia Pacific (Tokyo) regions. Customers can purchase X8aedz instances via Savings Plans, On-Demand instances, and Spot instances. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 X8aedz instance page or AWS news blog.
Quelle: aws.amazon.com

Amazon EC2 P6e-GB300 UltraServers accelerated by NVIDIA GB300 NVL72 are now generally available

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB300 UltraServers. P6e-GB300 UltraServers, accelerated by NVIDIA GB300 NVL72, provide 1.5x GPU memory and 1.5x FP4 compute (without sparsity) compared to P6e-GB200. 
Customers can optimize performance for the most powerful models in production with P6e-GB300 for applications that require higher context and implement emerging inference techniques like reasoning and Agentic AI.
To get started with P6e-GB300 UltraServers, please contact your AWS sales representative.
To learn more about P6e UltraServers and instances, visit Amazon EC2 P6 instances.
Quelle: aws.amazon.com

Announcing Amazon Nova 2 Sonic for real-time conversational AI

Today, Amazon announces the availability of Amazon Nova 2 Sonic, our speech-to-speech model for natural, real-time conversational AI that delivers industry leading quality and price for voice-based conversational AI. It offers best-in-class streaming speech understanding with robustness to background noise and users’ speaking styles, efficient dialog handling, and speech generation with expressive voices that can speak natively in multiple languages (Polyglot voices). It has superior reasoning, instruction following, and tool invocation accuracy over the previous model.
Nova 2 Sonic builds on the capabilities introduced in the original Nova Sonic model with new features including expanded language support (Portuguese and Hindi), polyglot voices that enable the model to speak different languages with native expressivity using the same voice, and turn-taking controllability to allow developers to set low, medium, or high pause sensitivity. The model also adds cross-modal interaction, allowing users to seamlessly switch between voice and text in the same session, asynchronous tool calling to support multi-step tasks without interrupting conversation flow, and a one-million token context window for sustained interactions.
Developers can integrate Nova Sonic 2 directly into real-time voice systems using Amazon Bedrock’s bidirectional streaming API. Nova Sonic 2 now also seamlessly integrates with Amazon Connect and other leading telephony providers, including Vonage, Twilio, and AudioCodes, as well as open source frameworks such as LiveKit and Pipecat.
Amazon Nova 2 Sonic is available in Amazon Bedrock in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Stockholm). To learn more, read the AWS News Blog and the Amazon Nova Sonic User Guide. To get started with Nova Sonic 2 in Amazon Bedrock, visit the Amazon Bedrock console.
Quelle: aws.amazon.com

Announcing the Apache Spark upgrade agent for Amazon EMR

AWS announces the Apache Spark upgrade agent, a new capability that accelerates Apache Spark version upgrades for Amazon EMR on EC2 and EMR Serverless. The agent converts complex upgrade processes that typically take months into projects spanning weeks through automated code analysis and transformation. Organizations invest substantial engineering resources analyzing API changes, resolving conflicts, and validating applications during Spark upgrades. The agent introduces conversational interfaces where engineers express upgrade requirements in natural language, while maintaining full control over code modifications. The Apache Spark upgrade agent automatically identifies API changes and behavioral modifications across PySpark and Scala applications. Engineers can initiate upgrades directly from SageMaker Unified Studio, Kiro CLI or IDE of their choice with the help of MCP (Model Context Protocol) compatibility. During the upgrade process, the agent analyzes existing code and suggests specific changes, and engineers can review and approve before implementation. The agent validates functional correctness through data quality validations. The agent currently supports upgrades from Spark 2.4 to 3.5 and maintains data processing accuracy throughout the upgrade process. The Apache Spark upgrade agent is now available in all AWS Regions where SageMaker Unified Studio is available. To start using the agent, visit SageMaker Unified Studio and select IDE Spaces or install the Kiro CLI. For detailed implementation guidance, reference documentation, and migration examples, visit the documentation.
Quelle: aws.amazon.com

Announcing Amazon EC2 General purpose M8azn instances (Preview)

Starting today, new general purpose high-frequency high-network Amazon Elastic Compute Cloud (Amazon EC2) M8azn instances are available for preview. These instances are powered by fifth generation AMD EPYC (formerly code named Turin) processors, offering the highest maximum CPU frequency, 5GHz in the cloud. The M8azn instances offer up to 2x compute performance versus previous generation M5zn instances. These instances also deliver 24% higher performance than M8a instances. M8azn instances are built on the AWS Nitro System, a collection of hardware and software innovations designed by AWS. The AWS Nitro System enables the delivery of efficient, flexible, and secure cloud services with isolated multitenancy, private networking, and fast local storage. These instances are ideal for applications such as gaming, high-performance computing, high-frequency trading (HFT), CI/CD, and simulation modeling for the automotive, aerospace, energy, and telecommunication industries. To learn more or request access to the M8azn instances preview, visit the Amazon EC2 M8a page.
Quelle: aws.amazon.com

AWS Transform for mainframe delivers new testing automation capabilities

AWS Transform for mainframe now offers test planning and automation features to accelerate mainframe modernization projects. New capabilities include automated test plan generation, test data collection scripts, and test case automation scripts, alongside functional test environment tools for continuous delivery and regression testing, helping accelerate and de-risk testing and validation during mainframe modernization projects. The new capabilities address key testing challenges across the modernization lifecycle, reducing the time and effort required for mainframe modernization testing, which typically consumes over 50% of project duration. Automated test plan generation helps teams reduce upfront planning efforts and align on critical functional tests needed to mitigate risk and ensure modernization success, while test data collection scripts accelerate the error-prone, complex process of capturing mainframe data. Test automation scripts then enable scalable execution of test cases by automating test environment staging, test case execution, and results validation against expected outcomes. By automating complex testing tasks and reducing dependency on scarce mainframe expertise, organizations can now modernize their applications with greater confidence while improving accuracy through consistent, automated processes. The new testing capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions. To learn more about automated testing in AWS Transform for mainframe, and how it can help your organization accelerate modernization, read the AWS News Blog, visit the AWS Transform for mainframe product page, or explore the AWS Transform User Guide.
Quelle: aws.amazon.com

AWS launches AWS Transform custom to accelerate organization-wide application modernization

AWS Transform custom is now generally available, accelerating organization-specific code and application modernization at scale using agentic AI. AWS Transform is the first agentic AI service to accelerate the transformation of Windows, mainframe, VMware, and more—reducing technical debt and making your tech stack AI-ready. Technical debt accumulates when organizations maintain legacy systems and outdated code, requiring them to allocate 20-30% of their software development resources to repeatable, cross-codebase transformation tasks that must be performed manually. AWS Transform can automate repeatable transformations of version upgrades, runtime migrations, framework transitions, and language translations at scale, reducing execution time by over 80% in many cases while eliminating the need for specialized automation expertise.
The custom transformation agent in AWS Transform provides both pre-built and custom solutions. It includes out-of-the-box transformations for common scenarios, such as Python and Node.js runtime upgrades, Lambda function modernization, AWS SDK updates across multiple languages, and Java 8 to 17 upgrades (supporting any build system including Gradle and Maven). For organization-specific needs, teams can define custom transformations using natural language, reference documents, and code samples. Users can trigger autonomous transformations with a simple one-line CLI command, which can be scripted or embedded into any existing pipeline or workflow. Within your organization, the agent continually learns from developer feedback and execution results, improving transformation accuracy and tightly aligning the agent’s performance with your organization’s preferences. This approach enables organizations to systematically address technical debt at scale, with the agent continually improving while developers can focus on innovation and high-impact tasks.
AWS Transform custom is now available in the US East (N. Virginia) AWS Region.
To learn more, visit the user guide, overview page, and pricing page.
Quelle: aws.amazon.com

AWS Transform expands .NET transformation capabilities and enhances developer experience

Today, AWS announces the general availability of expanded .NET transformation capabilities and an enhanced developer experience in AWS Transform. Customers can now modernize .NET Framework and .NET code to .NET 10 or .NET Standard. New transformation capabilities include UI porting of ASP.NET Web Forms to Blazor on ASP.NET Core and porting Entity Framework ORM code. The new developer experience, available with the AWS Toolkit for Visual Studio 2026 or 2022, is customizable, interactive, and iterative. It includes an editable transformation plan, estimated transformation time, real-time updates during transformation, the ability to repeat transformations with a revised plan, and next steps markdown for easy handoff to AI code companions. With these enhancements, AWS Transform provides a path to modern .NET for more project types, supports the latest releases of .NET and Visual Studio, and gives developers oversight and control of transformations.
Developers can now streamline their .NET modernization through an enhanced IDE experience. The process begins with automated code analysis that produces a customizable transformation plan. Developers can customize the transformation plan, such as fine-tuning package updates. Throughout the transformation, they benefit from transparent progress tracking and detailed activity logs. Upon completion, developers receive a Next Steps document that outlines remaining tasks, including Linux readiness requirements, which they can address through additional AWS Transform iterations or by leveraging AI code companion tools such as Kiro.
AWS Transform is available in the following AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London).
To get started with AWS Transform, refer to the AWS Transform documentation.
Quelle: aws.amazon.com