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Amazon ECS introduces Early Success Criteria for service deployments

Amazon Elastic Container Service (Amazon ECS) now supports Early Success Criteria for rolling service deployments, giving you the flexibility to define when a deployment is considered successful based on your confidence level and the operational needs of your workload. This can help you complete deployments sooner and unblock subsequent deployments, CI/CD pipelines, and other dependent operations.
With Early Success Criteria, you configure the healthy percent – the proportion of desired tasks that must be running and healthy on the target service revision before the deployment is marked successful. For example, with a desired count of 100 and a healthy percent of 90%, Amazon ECS marks the deployment successful after 90 tasks are healthy and continues launching the remaining tasks through regular service scaling, outside the deployment lifecycle. This can benefit workloads running on specialized or constrained capacity, such as GPU-accelerated inference workloads, where hardware availability can extend task launch times. Early Success Criteria also gives you more control over how long deployment rollback monitoring applies, allowing it to protect the deployment until your configured success criteria are met while subsequent scale-out continues through regular service scaling. You can also choose how Amazon ECS handles source service revision cleanup using BLOCKING or DEFERRED. With BLOCKING, Amazon ECS completes source revision cleanup before declaring success. With DEFERRED, Amazon ECS declares success when the criteria are met and drains source revision tasks asynchronously outside the deployment. This benefits services with active long-lived connections or task scale-in protection, where source revision tasks may need to remain running without holding the deployment open.
The feature is available with the rolling deployment strategy in all AWS Commercial and AWS GovCloud (US) Regions. You can configure Early Success Criteria for new and existing Amazon ECS services using the AWS Management Console, AWS CLI, AWS SDKs, and infrastructure as code (IaC) tools. To learn more, see our documentation.
Quelle: aws.amazon.com

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Amazon EC2 now supports specifying compatible instance types on AMIs

Amazon EC2 now enables AMI owners to define which instance types are compatible with their AMIs. Owners can specify supported instance types, unsupported instance types, or both — and any launch attempt on a non-permitted instance type is automatically blocked.
AMI owners now have a built-in way to prevent launches on instances that are not compatible with their AMIs. This reduces the risk of failed launches due to incompatible instance-AMI pairings. By default, an AMI can be launched on any instance type, so existing workflows remain unaffected until restrictions are explicitly applied.
This feature is available in all AWS Regions at no additional cost. To learn more, please visit the documentation.
Quelle: aws.amazon.com

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Amazon Bedrock Managed Knowledge Base now supports automatic sync scheduling for data source connectors

AWS announces automatic sync scheduling for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service that handles data ingestion, storage optimization, and advanced retrieval without requiring you to manage vector databases or data pipelines. Previously, keeping your knowledge base current required manually triggering a sync each time your source data changed or building a custom solution. Now, you can configure daily, weekly, or monthly sync schedules for all native data source connectors, so your AI agents always retrieve the most up-to-date information. With automatic sync scheduling, you can match your sync frequency to how often your source content changes. For example, set a daily sync for a rapidly evolving customer support knowledge base in Confluence, a weekly sync for SharePoint policy documents that update periodically, or a monthly sync for reference materials stored in Amazon S3. This eliminates the need to build and maintain custom scheduling workarounds, reducing operational overhead while ensuring your retrieval-augmented generation applications stay grounded in current enterprise data. To learn more, see Sync scheduling for data sources in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page.
Quelle: aws.amazon.com

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Amazon Bedrock Managed Knowledge Base now supports ServiceNow as a native data source connector

AWS announces the ServiceNow data source connector for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service. Customers can now connect their ServiceNow instance to crawl knowledge articles and service catalog items directly into their managed knowledge base. Previously, bringing ServiceNow content into Bedrock Knowledge Bases required building and maintaining custom ingestion pipelines—now, you simply provide your ServiceNow instance credentials, and the connector handles data crawling, metadata extraction, and incremental sync automatically. The ServiceNow connector crawls knowledge articles and service catalog items, including file attachments, giving your AI agents access to the institutional knowledge already maintained in ServiceNow. You can scope crawls to specific knowledge bases, article categories, or service catalogs using sys ID inclusion lists, ensuring only relevant content is ingested. This makes it straightforward to power employee-facing IT assistants, HR helpdesks, or customer support agents grounded in your organization’s up-to-date ServiceNow content. To learn more, see ServiceNow data source in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page.
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Amazon Bedrock Managed Knowledge Base introduces user-managed setup for SharePoint, OneDrive, and Confluence data sources

AWS announces user-managed setup (3LO) for SharePoint, OneDrive, and Confluence data sources in Amazon Bedrock Managed Knowledge Base. Previously, configuring these data sources required generating 2LO credentials on the third-party side, which could be time-consuming and inaccessible for users who lack admin-level access to those systems. With user-managed setup, you simply sign in with your existing third-party credentials, and Amazon Bedrock Managed Knowledge Base handles authentication—allowing you to complete data source setup in just a few minutes. This streamlined experience lowers the barrier to getting started with managed knowledge bases. Teams that want to quickly prototype an AI assistant grounded in their SharePoint documentation, OneDrive files, or Confluence wikis no longer need to coordinate with IT administrators to obtain service account credentials. User-managed setup complements the existing service account authentication, giving you a faster path to connect your data while still preserving the programmatic, enterprise-grade option for production workloads. To get started, see the following resources:; SharePoint user-managed setup in the Amazon Bedrock User Guide, OneDrive user-managed setup in the Amazon Bedrock User Guide and, Confluence user-managed setup in the Amazon Bedrock User Guide
Quelle: aws.amazon.com