Schlagwort: cloud computing

Amazon RDS now supports the latest CU and GDR updates for Microsoft SQL Server

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports the latest General Distribution Release (GDR) updates for Microsoft SQL Server. This release includes support for Microsoft SQL Server 2016 SP3+GDR KB5065226 (RDS version 13.00.6470.1.v1), SQL Server 2017 CU31+GDR KB5065225 (RDS version 14.00.3505.1.v1), SQL Server 2019 CU32+GDR KB5065222 (RDS version 15.00.4445.1.v1) and SQL Server 2022 CU21 KB5065865 (RDS version 16.00.4215.2.v1). The GDR updates address vulnerabilities described in CVE-2025-47997, CVE-2025-55227, CVE-2024-21907. For additional information on the improvements and fixes included in these updates, see Microsoft documentation for KB5065226, KB5065225, KB5065222 and KB5065865. 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

Generative AI observability now generally available for Amazon CloudWatch

Amazon CloudWatch announces the general availability of generative AI observability, helping you monitor all components of AI applications and workloads, including agents deployed and operated with Amazon Bedrock AgentCore. This release expands beyond runtime monitoring to include complete observability across AgentCore’s Built-in Tools, Gateways, Memory, and Identity capabilities. DevOps teams and developers can now get an out-of-the-box view into latency, token usage, errors, and performance across all components of their AI workloads, from model invocations to agent operations. This feature is compatible with popular generative AI orchestration frameworks such as Strands Agents, LangChain, and LangGraph, offering flexibility with your choice of framework. With this new feature, CloudWatch enalbes developers to analyzes telemetry data across components of a generative AI application. Customers can monitor code execution patterns in Built-in Tools, track API transformation success rates through Gateways, analyze memory storage and retrieval patterns, and ensure secure agent behavior through Identity observability. The connected view helps developers quickly identify issues – from gaps in VectorDB to authentication failures – using end-to-end prompt tracing, curated metrics, and logs. Developers can monitor their entire agent fleet through the “AgentCore” section in the CloudWatch console, which integrates seamlessly with other CloudWatch capabilities including Application Signals, Alarms, Sensitive Data Protection, and Logs Insights. This feature is now available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more, visit documentation. There is no additional pricing for Gen AI Observability, existing CloudWatch pricing for underlying telemetry data applies.
Quelle: aws.amazon.com

Announcing vector search for Amazon ElastiCache

Vector search for Amazon ElastiCache is now generally available. Customers can now use ElastiCache to index, search, and update billions of high-dimensional vector embeddings from popular providers like Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI with latency as low as microseconds and up to 99% recall. Key use cases include semantic caching for large language models (LLMs) and multi-turn conversational agents, which significantly reduce latency and cost by caching semantically similar queries. Vector search for ElastiCache also powers agentic AI systems with Retrieval Augmented Generation (RAG) to ensure highly relevant results and consistently low latency across multiple retrieval steps. Additional use cases include recommendation engines, anomaly detection, and other applications that require efficient search across multiple data modalities. Vector search for ElastiCache is available with Valkey version 8.2 on node-based clusters in all AWS Regions at no additional cost. To get started, create a Valkey 8.2 cluster using the AWS Management Console, AWS Software Development Kit (SDK), or AWS Command Line Interface (CLI). You can also use vector search on your existing clusters by upgrading from any version of Valkey or Redis OSS to Valkey 8.2 in a few clicks with no downtime. To learn more about vector search for ElastiCache for Valkey read this blog and for a list of supported commands see the ElastiCache documentation. 
Quelle: aws.amazon.com

Amazon Bedrock AgentCore is now generally available

Amazon Bedrock AgentCore is an agentic platform to build, deploy and operate highly capable agents securely at scale using any framework, model, or protocol. AgentCore lets you build agents faster, enable agents to take actions across tools and data, run agents securely with low-latency and extended runtimes, and monitor agents in production – all without any infrastructure management. With general availability, all AgentCore services now have Virtual Private Cloud (VPC) support, enabling secure, private agent deployment. AgentCore Runtime builds on its preview capabilities of industry-leading eight-hour execution windows and complete session isolation by adding support for the Agent-to-Agent (A2A) protocol, with broader A2A support coming soon across all AgentCore services. AgentCore Gateway now connects to existing Model Context Protocol (MCP) servers in addition to transforming APIs and Lambda functions into agent-compatible tools. Gateway provides a single, secure endpoint for agents to discover and use tools without the need for custom integrations. AgentCore Identity now offers identity-aware authorization, secure vault storage for refresh tokens, and native integration with additional OAuth-enabled services so agents can securely act on behalf of users or by themselves with enhanced access controls. AgentCore Observability now delivers complete visibility into end-to-end agent execution and operational metrics across all AgentCore services through dashboards powered by Amazon CloudWatch, and it is OTEL compatible, offering seamless integration with Amazon CloudWatch and external observability providers like Dynatrace, Datadog, Arize Phoenix, LangSmith, and Langfuse. AgentCore works with any open source framework (CrewAI, LangGraph, LlamaIndex, Google ADK, OpenAI Agents SDK) and any model in or outside Amazon Bedrock, giving you freedom to use your preferred frameworks and models, and innovate with confidence. Amazon Bedrock AgentCore is available in nine AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). Learn more about AgentCore through the blog, deep dive using the AgentCore resources, and get started with the AgentCore Starter Toolkit. AgentCore offers consumption-based pricing with no upfront costs.
Quelle: aws.amazon.com

Amazon SageMaker AI Projects now supports custom template S3 provisioning

Amazon SageMaker AI Projects now supports provisioning custom machine learning (ML) project templates from Amazon S3. Administrators can now manage ML templates in SageMaker AI studio so data scientists can create standardized ML projects to meet their organizational needs. Data scientists can use Amazon SageMaker AI Projects to create standardized ML projects that meet organizational requirements and automate ML development workflows. Administrators define standardized ML project templates that include end-to-end development patterns. By provisioning custom templates from Amazon S3, administrators can define standardized project templates and provide access to these templates directly in the SageMaker AI studio for data scientists, ensuring all ML projects follow organizational standards. SageMaker AI Projects custom template S3 provisioning is available in all AWS Regions where SageMaker AI Projects is available. To learn more, visit SageMaker AI Projects documentation, and SageMaker AI Studio. 
Quelle: aws.amazon.com