Monat: April 2026

Amazon SageMaker Unified Studio now supports multiple code spaces within projects for IAM domains

Amazon SageMaker Unified Studio now lets data workers create and manage multiple code spaces (individually configured development environments) within a single project for IAM domains. Previously, projects were limited to one JupyterLab space and one Code Editor space embedded in the project. With this launch, you can now parallelly work on different workstreams or experiments with different compute and storage configuration needs, giving developers the flexibility they need as their workloads scale. For instance, data scientists can now work in parallel on any long running data transformation and model training workloads within the same project using separate spaces.
With multiple spaces, each one maintains its own persistent Amazon EBS volume, ensuring that your files, data, and session state are preserved independently. You can scale compute and storage up or down per space, pause and resume them at any time, and customize the runtime environment for each specific task. Spaces can either be opened in dedicated browser tabs or connected to a local IDE if you prefer your own development environment, with full functionality including Amazon Q paid tier support. This is particularly beneficial for builders who need isolated environments for parallel workstreams while still working within a single collaborative project. 
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more about code spaces in SageMaker Unified Studio projects, see Managing Code Spaces in the Amazon SageMaker User Guide.
Quelle: aws.amazon.com

Amazon EC2 for SQL Server HA now supports health notifications

Today, AWS announced that you can now receive notifications through the AWS Health Dashboard when Amazon EC2 for SQL Server cannot detect a valid SQL Server High Availability (HA) status.
This enhancement is particularly valuable for customers who register EC2 SQL HA clusters through AWS Console or CloudFormation automation to reduce license included costs. You can receive this notification through multiple channels, including AWS Health, Amazon EventBridge events, and email. These notifications will guide you to respond quickly, helping avoid unexpected billing costs or charges.
To learn more, access the High Availability for SQL Server on Amazon EC2 user guide for additional details.  This feature is accessible in all AWS Regions where Amazon EC2 SQL HA is available and the AWS GovCloud (US) Regions.
Quelle: aws.amazon.com

Amazon Bedrock AgentCore adds new features to help developers build agents faster

Today, Amazon Bedrock AgentCore introduces new features to help developers go from an idea to working agent prototype faster and manage the full agent lifecycle from a single platform: a managed harness (in preview), the AgentCore CLI, and AgentCore skills for coding assistants.
The managed harness (preview) lets developers define an agent by specifying a model, system prompt, and tools, then run it immediately with no orchestration code required. The harness manages the full agent loop: reasoning, tool selection, action execution, and response streaming. Each session gets its own microVM with filesystem and shell access. The harness is model agnostic with the ability to switch models mid-session. Any configuration set at create time can be overridden per invocation, so developers experiment without redeploying. When developers need full control, they can export the harness orchestration in Strands-based code. Filesystem persistence (preview) externalizes the local session state, allowing agents to suspend mid-task and resume exactly where they left off. As a prototype evolves, developers can easily add evaluations to measure quality, memory for personalization, or additional tools and skills. When it’s time to promote a validated concept, the AgentCore CLI deploys with the governance and audibility of infrastructure-as-code. AWS CDK is supported today as a resource manager, with Terraform coming soon. The AgentCore CLI has been optimized for coding assistant control, with pre-built skills that provide accurate, up-to-date AgentCore guidance. AgentCore skills are available today through Kiro Power, with support for Claude Code, Codex, and Cursor coming next week.
The managed harness (preview) in AgentCore is available in four AWS Regions: US West (Oregon), US East (N. Virginia), Europe (Frankfurt), and Asia Pacific (Sydney). The AgentCore CLI is available in 14 AWS Regions where AgentCore is available. There is no additional charge for the harness, CLI, or skills. Learn more through the blog, and visit the documentation to get started.
Quelle: aws.amazon.com

AWS Secrets Manager extends managed external secrets to MongoDB Atlas and Confluent Cloud

AWS Secrets Manager now supports managed external secrets for MongoDB Atlas and Confluent Cloud.
AWS Secrets Manager now supports managed external secrets for MongoDB Atlas and Confluent Cloud, enabling you to centrally manage and automatically rotate secrets for these third-party services directly from AWS Secrets Manager — without building or maintaining custom Lambda rotation functions.
The MongoDB Atlas integration supports two secret types: database user secrets (username-password authentication via SCRAM) and service account secrets (OAuth client ID and secret). The Confluent Cloud integration supports API key rotation for service accounts, with support for both cluster-scoped and cloud resource management keys. All integrations include automatic rotation enabled by default, eliminating hardcoded secrets and reducing the operational overhead of managing secrets across multiple platforms.
With managed external secrets, secret rotation is fully managed by AWS Secrets Manager using partner-provided rotation logic — no Lambda functions are deployed in your account. For example, a data pipeline using MongoDB Atlas and Confluent Kafka can now centralize secret management in AWS Secrets Manager, automatically rotating database and streaming platform secrets without modifying application code or managing separate rotation logic for each service.
MongoDB Atlas and Confluent Cloud integrations for managed external secrets are available in all AWS Regions where managed external secrets is supported, joining existing integrations with Salesforce, Snowflake, and BigID. To learn more, visit the AWS Secrets Manager managed external secrets documentation.
Quelle: aws.amazon.com

Amazon SageMaker AI now supports serverless model customization for Qwen3.5 models

Amazon SageMaker AI now supports serverless model customization for Qwen3.5, enabling you to fine-tune Qwen3.5 4B, 9B, and 27B parameter models using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Qwen3.5 is a popular open-weight model family from Alibaba Cloud. Before this launch, you could deploy these base models on SageMaker AI and now, you can also adapt them to your specific domains and workflows. 
Model customization enables you to tailor foundation models with your proprietary data so they more accurately reflect your domain knowledge, terminology, and quality standards. Rather than building models from scratch, fine-tuning lets you start from a capable base model and specialize it for your use cases, whether that’s improving accuracy on domain-specific tasks, aligning outputs with your organization’s tone, or improving performance on new tasks using your labeled data. 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 Qwen3.5 on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (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