Cloud Computing Köln

AWS Batch now supports bulk job cancellation and termination

AWS Batch now supports bulk job cancellation and termination, enabling you to cancel or terminate up to 50 jobs with a single API call. The new CancelJobs, TerminateJobs, and TerminateServiceJobs APIs reduce the operational complexity of managing large-scale batch workloads, letting you act on groups of jobs at once and receive per-job results in a single response. Additionally, ListJobs now returns isCancelled and isTerminated fields, and ListServiceJobs returns isTerminated, making it easier to track the lifecycle state of your jobs.
To get started, call CancelJobs for jobs in SUBMITTED, PENDING, or RUNNABLE states, or TerminateJobs and TerminateServiceJobs for jobs in any state, including STARTING and RUNNING. All three APIs accept up to 50 job IDs and work with individual and array jobs. You can access them through the AWS CLI or AWS SDKs.
Bulk job cancellation and termination is available in all AWS Regions where AWS Batch is available. For more information, see CancelJobs, TerminateJobs, and TerminateServiceJobs pages in the AWS Batch API Reference Guide.
Quelle: aws.amazon.com

AWS HealthOmics now supports IAM session policies

AWS HealthOmics now supports IAM session policies, enabling you to restrict permissions for individual runs without creating and managing multiple IAM roles. Until now, there was no way to dynamically scope down permissions for a single run, requiring you to create a separate IAM role for each tenant or run. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows.
An IAM session policy is an inline policy that limits the maximum permissions of a run without modifying the underlying service role. Your effective permissions during a run are the intersection of permissions allowed by both the underlying identity-based policy and the temporary session policy. For example, if you operate a multi-tenant application, you can pass a session policy that limits a run’s access to only that tenant’s Amazon S3 buckets, without provisioning a dedicated IAM role for that tenant. You can also use IAM session policies to grant temporary access to specific Amazon S3 objects for a single run, and isolate access to sensitive resources on a per-run basis.
IAM session policy support is available in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Tokyo, Singapore, Seoul). To learn how to configure IAM session policies for your runs, visit the Permissions section of the  AWS HealthOmics User Guide. To learn more about the service, visit  AWS HealthOmics.
Quelle: aws.amazon.com

AWS Transfer Family now supports source IP preservation for SFTP servers behind a Network Load Balancer (NLB)

AWS Transfer Family now preserves the client’s source IP address using Proxy Protocol v2 (PPv2) when you place a Network Load Balancer (NLB) in front of your SFTP server that uses a VPC-hosted endpoint. You can now retain visibility of the client’s source IP for IP-based auditing, access controls, and compliance when you use your own NLB.
Previously, an NLB replaced the client’s source IP with its own private IP address, so your Transfer Family logs and events recorded the NLB’s address instead of the client’s source IP. Because the NLB’s private IP was the address presented to your custom identity provider during authentication, you couldn’t authorize users based on their true source IP. With this launch, you can enable source IP preservation on your SFTP server so that the client’s source IP is preserved. The preserved source IP is recorded in your logs and events and presented to your custom identity provider during authentication. You can enable the feature on each Transfer Family server individually through the console, CLI, or API.
Source IP preservation for SFTP servers is available in all AWS Regions where AWS Transfer Family is available. To get started, visit the AWS Transfer Family console or use the AWS CLI/SDK. To learn more, visit the Transfer Family User Guide.
Quelle: aws.amazon.com

Amazon Connect Customer can now import evaluation form PDFs using AI

Amazon Connect Customer now lets managers import a PDF of an evaluation form from a third-party quality management application, and automatically recreates it in Connect using AI, making it easier to migrate an existing quality program into Connect. Instead of rebuilding an existing form question by question, you upload the PDF and choose the preferred scoring method, either percentage or points-based, and Connect Customer automatically extracts the sections, questions, answer options, and scoring into a draft evaluation form. You can also provide natural language instructions to import the form more accurately or to adjust it as it’s created — for example, splitting a broad question into simpler ones — minimizing the manual edits needed before you activate the form.
This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Frankfurt). To learn more, please visit our documentation and our webpage. For information about Amazon Connect Customer pricing, please visit our pricing page.
Quelle: aws.amazon.com

Amazon SageMaker AI now supports serverless model customization for NVIDIA Nemotron 3.5 Lightning

Amazon SageMaker AI now supports serverless model customization for the NVIDIA Nemotron 3.5 Lightning model using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement fine-tuning (RFT). This is one of the latest open-weight models from NVIDIA and employs a hybrid Mixture-of-Experts architecture with 3B active parameters and 30B parameters in total. In addition to deploying this model on SageMaker AI, you can now adapt it to your specific domains and workflows.
Model customization enables you to tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, reducing cost and latency. You can use labeled data with SFT to improve accuracy on domain-specific tasks, preference data with DPO to align outputs with your organization’s tone, or reward signals with RFT to enhance performance on new tasks. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use.
Serverless model customization for NVIDIA Nemotron 3.5 Lightning on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access. To learn more, see the Amazon SageMaker AI model customization documentation. 
Quelle: aws.amazon.com