Cloud Computing Köln

Apache Spark lineage now available in Amazon SageMaker Unified Studio for IDC based domains

Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. Data Lineage provides you with the information you need to identify the root cause of complex issues and understand the impact of changes. This feature supports lineage capture of schema and transformations of data assets and columns from Spark executions in EMR-EC2, EMR-Serverless, EMR-EKS, and AWS Glue. You can then explore this lineage visually as a graph in SageMaker Unified Studio or query it using APIs. You can also use lineage to compare transformations across Spark job’s history. Spark lineage is available in all existing SageMaker Unified Studio regions. For detailed information on how to get started with lineage using these new features, refer to the documentation.
Quelle: aws.amazon.com

Structured outputs now available in Amazon Bedrock

Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient. Structured outputs helps with common production tasks such as extracting key fields and powering workflows that use APIs or tools, where small formatting errors can break downstream systems. By ensuring schema compliance, it reduces the need for custom validation logic and lowers operational overhead through fewer failed requests and retries—so you can confidently deploy AI applications that require predictable, machine-readable outputs. You can use structured outputs in two ways: define a JSON schema that describes the response format you want, or use strict tool definitions to ensure a model’s tool calls match your specifications. Structured outputs is generally available for Anthropic Claude 4.5 models and select open-weight models across the Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream APIs in all commercial AWS Regions where Amazon Bedrock is supported. To learn more about structured outputs and the supported models, visit the Amazon Bedrock documentation.
Quelle: aws.amazon.com

Amazon EC2 G7e instances now available in US West (Oregon) region

Starting today, Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in US West (Oregon) region. G7e instances offer up to 2.3x inference performance compared to G6e.
Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads.
You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia) and US East (Ohio). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.
Quelle: aws.amazon.com

Cartesia Sonic 3 text-to-speech model is now available on Amazon SageMaker JumpStart

Cartesia’s Sonic 3 model is now available in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. Sonic 3 is Cartesia’s latest state space model (SSM) for streaming text-to-speech (TTS), delivering high naturalness, accurate transcript following, and industry-leading latency with fine-grained control over volume, speed, and emotion.
Sonic 3 supports 42 languages and provides advanced controllability through API parameters and SSML tags for volume, speed, and emotion adjustments. The model includes natural laughter support, stable voices optimized for voice agents, and emotive voices for expressive characters. With sub-100ms latency, Sonic 3 enables real-time conversational AI that captures human speech nuances including emotions and tonal shifts. With SageMaker JumpStart, customers can deploy Sonic 3 with just a few clicks to address their voice AI use cases. To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker Studio or use the SageMaker Python SDK to deploy the model 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

AWS Batch now supports unmanaged compute environments for Amazon EKS

AWS Batch now extends its job scheduling capabilities to unmanaged compute environments on Amazon EKS. With unmanaged EKS compute environments, you can leverage AWS Batch’s job orchestration while maintaining full control over your Kubernetes infrastructure for security, compliance, or operational requirements. With this capability, you can create unmanaged compute environments through CreateComputeEnvironment API and AWS Batch console by selecting your existing EKS cluster and specifying a Kubernetes namespace, then associate your EKS nodes with the compute environment using kubectl labeling. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Unmanaged compute environments on Amazon EKS are available today in all AWS regions where AWS Batch is available. For more information, see the AWS Batch User Guide.
Quelle: aws.amazon.com