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AWS Transform for migrations expands support for existing landing zones

AWS Transform now supports more types of existing AWS environments when building landing zones for your migration, including AWS Control Tower landing zones and landing zones built with AWS Organizations. AWS Transform runs a foundation gap analysis of your current environment against AWS best practices and expands your landing zone on top of what’s already in your target accounts, so your team can start migrating sooner.
AWS Transform also now supports an Infrastructure as Code (IaC)-only mode for building landing zones for your migration. In this mode, AWS Transform analyzes your organization, generates recommendations, and produces IaC tailored to your migration plan in AWS CDK, Terraform, or Landing Zone Accelerator (LZA) format. IaC-only mode is designed for organizations that prefer not to grant AWS Transform direct access to their management account. AWS Transform can now connect through a delegated administrator (DA) account instead. It also benefits customers who manage their own deployment pipeline and don’t need the agent to deploy automatically.
The AWS Transform for migrations landing zone agent automates landing zone design end to end, including foundation setup, organizational unit (OU) and account structure, service control policies (SCPs), and workload accounts aligned to your migration planning and waves. You can deploy directly or generate IaC for your own pipeline.
These new capabilities are available in all target AWS Regions supported by AWS Transform for migrations.
To learn more, please visit the AWS Transform User Guide.
Quelle: aws.amazon.com

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AWS Clean Rooms now supports Databricks as a data source

AWS Clean Rooms now supports Databricks as a data source, so collaboration members and their partners can securely analyze data managed in Databricks without moving or revealing the source data. Members query a Databricks table shared through Delta Sharing directly in a collaboration. Databricks joins AWS Glue, Snowflake, and Amazon Athena as supported data sources in AWS Clean Rooms.
Previously, members with data in Databricks had to export or replicate it into Amazon S3 to use it as a table in a collaboration, which added pipeline work and an extra copy of the data to govern. Now members can register a Databricks table directly by providing the workspace URL, schema, and table along with a Delta Sharing share, and make it available to a collaboration. For example, a retailer that manages its customer data in Databricks can collaborate with a media partner on campaign measurement in AWS Clean Rooms, running approved analyses against its Databricks data without first building and maintaining an export to S3.
AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
Quelle: aws.amazon.com

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Structured outputs for Claude models available on Amazon Bedrock

Amazon Bedrock now supports structured outputs for all Anthropic Claude models across both the bedrock-runtime and the bedrock-mantle endpoint. Whether you call Claude through boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure, without custom parsing, retry logic, or post-processing.
With structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always match your application’s data model.
To learn more, see the Amazon Bedrock documentation . Structured outputs is available where claude models are available. For more information check the regional availability details page.
Quelle: aws.amazon.com

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AWS announces TorchNeuron: Native PyTorch Support for AWS Trainium (Public Beta)

Today, AWS launched the public beta of TorchNeuron, an open source native PyTorch backend for AWS Trainium. Trainium is a portfolio of AI chips designed to deliver high performance and cost efficiency for deep learning AI training and inference workloads. TorchNeuron enables AI scientists and researchers to experiment and build models with PyTorch natively on Trainium.
With TorchNeuron, developers can enable their PyTorch code on Trainium simply by switching to .to(‘neuron’) while maintaining full PyTorch compatibility with eager execution, torch.compile, and standard PyTorch APIs. Additionally, TorchNeuron integrates with Neuron Kernel Interface (NKI) to enable custom operators and performance optimization for Trainium hardware.
TorchNeuron is available as open source through the TorchNeuron github repo for Amazon EC2 Trainium2 instances and Amazon EC2 Trainium3 instances.
To learn more about TorchNeuron and AWS Trainium accelerators, visit the AWS Neuron page.
Quelle: aws.amazon.com

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Polly Generative engine upgraded with more natural speech, 14 new voices, and 2 new languages

Today, we are introducing a new model behind the Amazon Polly Generative engine that delivers more natural-sounding, accurate speech output with low latency. With this upgrade, you get access to an enhanced voice portfolio with higher overall quality, 14 new voices across nine locales, and two new languages—Hindi and Finnish—now generally available on the Generative engine for the first time. New voices include Kenneth and Nate (US English), Russell and Curtis (Australian English), Kiara and Arjun (Hindi and Indian English), Tina (German), Carlos (US Spanish), Leo and Carolina (Brazilian Portuguese), Hyunduk (Korean), and Suvi (Finnish).
Amazon Polly is a fully managed service that turns text into natural-sounding speech. This drop-in model upgrade makes Generative voices more natural and accurate. Complex content such as numbers, email addresses, and street addresses is designed to sound more reliable, reducing the need to fine-tune speech output. You can continue to use Speech Synthesis Markup Language (SSML) tags to control speech output on the Generative engine, just as before. The low-latency streaming API makes it easy to build contact center experiences, chatbots, and large language model (LLM) based agentic voice applications for real-time conversations. The richness and variety of voice options are also well suited for news, media, entertainment, e-learning, and accessibility use cases. This launch also expands the Amazon Polly Generative offering to AWS GovCloud, providing more options for public sector and regulated industries.
All new Generative voices are now available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Zurich), Europe (London), Asia Pacific (Sydney), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo), Canada (Central), and AWS GovCloud (US-West).
To hear how Polly voices sound, go to Amazon Polly Features. For more details on the Polly offerings and use, see the Amazon Polly documentation and pricing page.
Quelle: aws.amazon.com