Schlagwort: cloud computing

Amazon SageMaker Studio now sets up in seconds with model customization ready from the start

Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly. As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions. This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see Quick setup and Model Customization permissions setup in the Amazon SageMaker documentation.
Quelle: aws.amazon.com

AWS Deadline Cloud now supports persistent storage for Service Managed Fleets

AWS Deadline Cloud now supports persistent storage for Service-Managed Fleets (SMF), allowing you to maintain data across worker lifecycle events. AWS Deadline Cloud is a fully managed service that makes it easy for teams to run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming. Previously, Deadline Cloud SMF workers relied only on ephemeral storage, requiring software and assets to be reinstalled each time a worker was recycled or replaced. Now, Deadline Cloud attaches persistent Amazon Elastic Block Store (Amazon EBS) volumes to SMF workers, preserving Conda environments, Perforce workspaces, shader caches, and asset collections across worker lifecycle events. This reduces worker startup time and helps you complete jobs faster. You can configure the number of persistent volumes per worker and set a time-to-live (TTL) to control how long volumes are retained, giving you flexibility to balance storage costs with startup performance.  Persistent storage for SMF is available in all AWS Regions where Deadline Cloud is offered. Persistent volumes are priced the same as existing Service-Managed Fleets EBS pricing. See the Deadline Cloud pricing page for details. To learn more, visit the AWS Deadline Cloud product page or our user guide.
Quelle: aws.amazon.com

Amazon Quick now supports VPC connectivity for MCP connections

Amazon Quick now enables enterprise customers to connect their privately hosted Model Context Protocol (MCP) servers to Quick through Amazon Virtual Private Cloud (VPC). Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes for you and your entire team. Previously, Quick’s MCP support was limited to third-party hosted servers accessible over the public internet. With VPC support, organizations that host MCP servers on private networks for proprietary applications, custom data sources, and internal tools can now securely extend those capabilities to AI workflows in Quick. With VPC connectivity for MCP, you can connect Quick to MCP servers running on Amazon EC2, AWS Fargate, AWS Agentcore, or other compute within your private network without exposing them to the internet. During MCP connector creation, select your VPC connection and provide your MCP server URL. Once connected, your team interacts with private MCP servers through natural language in Quick, with all traffic routed securely through your VPC. VPC support for MCP servers is available in all AWS Regions where Amazon Quick is available. Learn more about Amazon Quick and try for free. To learn more about connecting private MCP servers, visit the MCP documentation and the VPC connectivity guide.
Quelle: aws.amazon.com

AWS HealthOmics now supports Nextflow version pinning at run time

AWS HealthOmics now allows customers to specify the Nextflow engine version at run time via the StartRun API, enabling customers to pin runs to a specific Nextflow version for controlled migration. With this launch, customers can select from supported Nextflow versions (22.04, 23.10, 24.10, 25.10, 26.04) through the new engine-settings parameter, giving explicit control at the point of execution. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Nextflow version pinning gives customers full control over when and how they adopt new engine versions. The run-time version override ensures that even when a workflow definition specifies a version via manifest.nextflowVersion in its config or profile, the StartRun API parameter takes precedence, enabling customers to test the same workflow across multiple engine versions without modifying workflow source code. Production workflows can remain on a validated engine version while development teams test newer versions in parallel, reducing the risk of unexpected behavior changes. This is valuable for regulated environments where pipeline validation is required before upgrading to a new engine version.
Nextflow version pinning at run time is now available for Nextflow workflow runs in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow engine settings documentation.
Quelle: aws.amazon.com

AWS HealthOmics now supports Nextflow version 26.04

AWS HealthOmics now supports Nextflow version 26.04, enabling customers to take advantage of new Nextflow features and enhancements: record types, the strict syntax parser, workflow output summaries, and agent logging mode. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
The strict syntax parser, now enabled by default in Nextflow v26.04, helps customers save compute time and costs by enforcing strict linting, consistent block structures, and unambiguous scoping, catching issues during pipeline initialization rather than hours into workflows. Record types allow workflow developers to write workflows with meaningful data names rather than keeping track of order of tuple elements, making workflows more readable, and less error-prone. Workflow output summary in JSON format simplifies integration with downstream tooling. Agent logging mode provides structured, minimal output optimized for AI-assisted workflow debugging and development.
Nextflow v26.04 is now available in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the AWS HealthOmics Nextflow workflow definition specifics documentation.
Quelle: aws.amazon.com