Tag: SysAdmin

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 integr

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 mult

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 fe

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 se

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

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

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.

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 computin

Starting today, Amazon EC2 M8i and M8i-flex instances are now available in Asia Pacific (New Zealand) Region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The M8i and M8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% better performance than M7i and M7i-flex instances, with even higher gains for specific workloads. The M8i and M8i-flex instances are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to M7i and M7i-flex instances. M8i-flex are the easiest way to get price performance benefits for a majority of general-purpose workloads like web and application servers, microservices, small and

Amazon Bedrock AgentCore Identity now allows customers the ability to reference existing AWS Secrets Manager secret ARNs directly in AgentCore Identity Credential Providers. Previously, AgentCore Identity used a service-managed secret approach, where secrets were created and managed by the service on the customer's behalf. This approach prevented customers from applying resource tags on create, encrypting secrets with a customer-managed key (CMK), or applying other organization-specific governance controls at the time of secret creation — causing friction for teams with strict governance requirements. Now, customers create and manage their secrets in AWS Secrets Manager using their own governance and compliance policies, including custom CMKs, tagging strategies, automatic rotation and resource policies, and then reference the existing secret ARN when configuring a Credential Provider in AgentCore Identity. This gives customers full ownership of how their se