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Amazon RDS now supports the latest CU for Microsoft SQL Server

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports the latest Cumulative Updates (CU) for Microsoft SQL Server. This release includes support for Microsoft SQL Server 2025 CU8 KB5104822 (RDS version 17.00.4075.5.v1).

For additional information on the improvements and fixes included in these updates, see Microsoft documentation for KB5104822. We recommend that you upgrade your Amazon RDS for SQL Server instances to apply these updates using Amazon RDS Management Console, or by using the AWS SDK or CLI. You can learn more about upgrading your database instance in the Amazon RDS SQL Server User Guide for upgrading your RDS Microsoft SQL Server DB engine.

Quelle: aws.amazon.com

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AWS DataSync Enhanced mode now supports FSx for OpenZFS, NFS 4.2, and object metadata controls

AWS DataSync Enhanced mode now supports Amazon FSx for OpenZFS as a source or destination location, NFS version 4.2 for on-premises NFS servers and FSx for OpenZFS file systems, and a new ObjectMetadata option for object storage transfers. Previously, customers transferring data to or from FSx for OpenZFS were limited to Basic mode.
NFS 4.2 lets you transfer data from NFS servers on the latest version without reconfiguring them to an older protocol version. The ObjectMetadata option gives you control over whether an object’s user metadata is copied to the destination, which helps when destination metadata must stay intact, such as buckets served by AWS Storage Gateway, or when source metadata keys are not compatible with the destination, such as Azure Blob Storage. Existing tasks remain unaffected and continue to run as scheduled.
These capabilities are available for Enhanced mode tasks in all AWS Regions where AWS DataSync Enhanced mode is supported. To learn more, visit the AWS DataSync documentation.
Quelle: aws.amazon.com

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Bring deep SageMaker AI expertise to your coding agent with the AWS AI/ML skill in the Agent Toolkit for AWS

Your coding agent can select, train, optimize and deploy models on Amazon SageMaker AI while working across the rest of your AWS environment in a single natural-language workflow. The AWS AI/ML skill is now a core skill in the Agent Toolkit for AWS, so it works alongside the toolkit’s other AWS skills like EC2, Bedrock, and more.
We released the new AWS AI/ML skill on the Agent Toolkit for AWS. The toolkit is now the single, supported integration point, replacing the previous AWS Labs skills and plugins, with a consistent and easy install and update path. The skill is also pre-installed into SageMaker Studio JupyterLab for use with Kiro or your coding agent of choice.
Moreover, we extended the SageMaker AI capability areas supported by skills.

Inference optimization: optimize deployed models for latency, throughput, and cost

Model customization: end-to-end guided fine-tuning workflows, from use case definition through training, evaluation and deployment

Serverful training for both traditional ML and generative AI

MLflow: experiment tracking and model lifecycle management

JumpStart model deployment and model recommendations: deploy pre-built JumpStart models and get guided recommendations on the right model for the use case

Processing jobs: data preparation and ETL on managed compute

The AWS AI/ML skill is available at no additional charge; you pay only for the AWS resources your agents use. To get started, install the skill from the Agent Toolkit for AWS on GitHub, or use it pre-installed in SageMaker Studio JupyterLab. To learn more about using a coding assistant with your SageMaker AI workflows, see the Amazon SageMaker AI documentation.
Quelle: aws.amazon.com

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Announcing consumption-based pricing for short-term memory in Amazon Bedrock AgentCore

Announcing consumption-based pricing for short-term memory in Amazon Bedrock AgentCore
Amazon Bedrock AgentCore memory now prices short-term memory (STM) based on the data your agents ingest, retrieve, and store, replacing the previous flat rate per event. AgentCore memory gives agents in-session context through short-term memory and persistent knowledge across sessions through long-term memory. With consumption-based pricing, developers building agents that write frequent, small events pay in proportion to what they use.
STM is priced across three dimensions: ingestion at $1.00 per GB, retrieval at $0.20 per GB, and storage at $0.10 per GB-month. Ingestion and retrieval are billed per event with a 12 KB minimum and 64 KB maximum billable size, while storage is billed on each event’s actual size. Most workloads see lower costs, with small-payload use cases seeing the largest reductions. You can manage storage costs by setting a shorter time to live (TTL) on your memory resources. Long-term memory pricing is unchanged.
Consumption-based pricing for AgentCore Memory short-term memory is available in all AWS Regions where AgentCore Memory is offered. To get started, visit the Amazon Bedrock AgentCore pricing page.
Quelle: aws.amazon.com