Kategorie: Amazon AWS

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

Amazon EC2 M8azn instances are now available in Europe (Ireland) Region

Starting today, Amazon EC2 M8azn instances are now available in Europe (Ireland) Region. These general purpose high-frequency high-network instances are powered by fifth generation AMD EPYC (formerly code named Turin) processors and offer the highest maximum CPU frequency, 5GHz in the cloud. M8azn instances offer up to 2x compute performance compared to previous generation M5zn instances, and up to 24% higher performance than M8a instances. M8azn instances deliver up to 4.3x higher memory bandwidth and 10x larger L3 cache compared to M5zn instances allowing latency-sensitive and compute-intensive workloads to achieve results faster. These instances also offer up to 2x networking throughput and up to 3x EBS throughput versus M5zn instances. Built on the AWS Nitro System using sixth generation Nitro Cards, these instances are ideal for applications such as real-time financial analytics, high-performance computing, high-frequency trading (HFT), CI/CD, intensive gaming, and simulation modeling for the automotive, aerospace, energy, and telecommunication industries. M8azn instances are available in 9 sizes ranging from 2 to 96 vCPUs with up to 384 GiB of memory, including two bare metal variants. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 M8azn instance page.
 
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now supports EFA-only network interfaces

Amazon SageMaker HyperPod now supports EFA-only network interfaces for cluster instance groups, enabling you to configure dedicated Elastic Fabric Adapter (EFA) devices without the traditional Elastic Network Adapter (ENA) for IP networking. SageMaker HyperPod is a purpose-built infrastructure for AI/ML model development that provides a resilient, high-performance environment with built-in fault tolerance and automated cluster recovery. Now with EFA-only, you can scale AI/ML clusters further without risking IP address exhaustion in your VPC.
When running large-scale distributed training workloads, inter-node communication bandwidth is critical to training performance. SageMaker HyperPod cluster instances support multiple EFA-capable network interfaces, but configuring them with the standard efa interface type attaches both an EFA device and an ENA device (for IP networking) to each interface — even when IP networking is only needed on a subset of interfaces within a node. The efa interface type inescapably consumes IP addresses in your subnet for each ENA device attached, which can lead to IP address exhaustion and limit the number of nodes you can deploy within a single subnet. With this launch, you can now set efa-only when configuring network interfaces for your HyperPod cluster instance groups. This option allocates the network interface exclusively for EFA traffic without attaching an ENA device, allowing you to maximize the number of EFA interfaces dedicated to low-latency, high-throughput inter-node communication. Because EFA-only interfaces do not require IP addresses, you can scale to larger clusters within the same subnets without encountering IP exhaustion. This configuration is particularly beneficial for large-scale distributed training jobs where inter-node communication bandwidth is critical and dedicated IP networking on every interface is not required.
To enable EFA-only, specify efa-only in the ClusterNetworkInterface configuration when creating or updating your HyperPod cluster via the CreateCluster/UpdateCluster API. EFA-only is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more, see ClusterNetworkInterface in the Amazon SageMaker API Reference.
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now offers troubleshooting skills for AI coding assistants

Amazon SageMaker HyperPod now provides troubleshooting skills that bring expert-level AI/ML cluster diagnostics directly into AI coding assistants such as Claude Code, Cursor, and Kiro. SageMaker HyperPod is a purpose-built infrastructure for developing, training, and deploying foundation models at scale. It provides a resilient and performant environment with built-in fault tolerance, and automated cluster recovery, reducing the undifferentiated heavy lifting of managing large-scale AI/ML infrastructure. HyperPod skills enable you to diagnose and resolve cluster issues through natural language, reducing the time and expertise required to troubleshoot distributed training and inference infrastructure.
Debugging GPU hardware faults, diagnosing NCCL communication failures, and identifying performance bottlenecks across large distributed clusters remains complex and time-consuming. Operators often need to manually SSM into nodes, parse logs across dozens of instances, and cross-reference documentation. The new HyperPod troubleshooting skills help with faster time to resolution with capabilities spanning cluster health validation, hardware and communication diagnostics, software version drifts, and automated diagnostic reporting. Each skill encodes AWS best practices into structured diagnostic workflows that systematically guides AI agents to collect evidence from your cluster nodes via AWS Systems Manager, analyze patterns, and provide actionable recommendations. The skills work with your existing HyperPod infrastructure — no modifications are required.
The HyperPod troubleshooting skills are open source and available today for both Slurm and Amazon EKS orchestrated HyperPod clusters via the SageMaker AI skills plugin. To get started, visit the AWSLabs github repository to install the sagemaker-ai plugin in your preferred coding assistant.
Quelle: aws.amazon.com

Amazon SageMaker adds permissions boundaries for SCP compliance

Amazon SageMaker Unified Studio now supports custom IAM permissions boundaries, so organizations that enforce Service Control Policies (SCPs) requiring permissions boundaries on all IAM roles can adopt SageMaker Unified Studio without modifying their security posture. When a user creates a project, SageMaker Unified Studio provisions three IAM roles: a project user role, an Amazon Bedrock service role, and a Bedrock Lambda execution role. With this launch, administrators can specify a permissions boundary in the Tooling blueprint configuration, and all three roles are created with that permissions boundary attached. This satisfies SCP requirements at creation time, and project provisioning succeeds without administrator intervention. The permissions boundary also limits what the provisioned roles can do, so administrators retain control over project-level permissions even as new projects are created. Because the permissions boundary is set at the blueprint level, it applies to every new project automatically. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Manage Tooling blueprint parameters documentation.
Quelle: aws.amazon.com

Quick Research now supports customer managed keys

Amazon Quick Research now enables customers to encrypt their data using customer-managed keys (CMK) through AWS Key Management Service (KMS).
This enhancement allows organizations with strict security and compliance requirements to manage their own encryption keys. With customer-managed keys, you gain enhanced security control and comprehensive audit capabilities through AWS CloudTrail integration. You can encrypt your data with your own KMS keys, trace all data access for security auditing, and revoke access to compromised keys within 15 minutes during security incidents. This feature supports multiple CMKs with one default key per AWS account per region, providing the flexibility to manage encryption across different datasets while maintaining granular control over your sensitive business intelligence data.
Customer-managed keys must be created in the same AWS account and region as your Quick resources, and only symmetric AWS KMS keys are supported.
This feature is generally available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick Research detail page.
Quelle: aws.amazon.com