Back to Archive

AWS News - 2026-05-20

2026-05-20
最終更新: 2026-05-25 21:25:58 JST

AI による概要

17 記事

この日は AI エージェント基盤、セキュリティガバナンス、リージョン拡張が中心でした。機械学習では Amazon Nova Sonic を用いたスケーラブルな音声エージェント設計、Kiro CLI の会話メモリ拡張、SageMaker Feature Store の新機能、Bedrock でのプログラマティックツール呼び出しが解説されました。アーキテクチャブログでは Amazon EKS を用いた機械学習事例や、EC2 G7e での生成 AI 動画推論最適化を紹介。セキュリティブログではパターンベースのポリシー・アズ・コードによる IaC ガバナンス、AWS Organizations からの不正アカウント離脱防止 (CIRT) を扱いました。What's New では Amazon Inspector の台北リージョン提供、MWAA の Airflow 3.2 対応、ECS のデプロイ一時停止・再開制御、SageMaker HyperPod の推論データキャプチャ、イスタンブールの新 Local Zone GA などが発表されました。

主要トピック

  • AI エージェント: Nova Sonic 音声エージェント、Bedrock AgentCore 活用

  • セキュリティ: IaC ポリシー・アズ・コード、Organizations 不正離脱防止

  • コンテナ: ECS のデプロイ一時停止・再開制御

  • 運用/リージョン: Inspector 台北対応、イスタンブール Local Zone GA

  • ML 運用: SageMaker HyperPod の推論データキャプチャ、MWAA 3.2

AI (Claude Opus 4.8) が生成 · 2026-07-04 JST

AWS What's New

Amazon Managed Grafana now supports dual-stack connectivity (IPv6 and IPv4)

Amazon Managed Grafana now supports dual-stack connectivity, enabling workspaces to communicate over both Internet Protocol version 4 (IPv4) and Internet Protocol version 6 (IPv6). Dual-stack mode is available for workspaces running Grafana version 10.4 or later.

With dual-stack support, customers can simplify their network stack by eliminating the need to manage overlapping address spaces in their VPCs. Customers migrating to IPv6 can connect to their Grafana workspaces over IPv6 while maintaining IPv4 compatibility, and those not yet on IPv6 can continue using IPv4-only connections. This is especially beneficial as the continued growth of the internet exhausts available IPv4 addresses. 

Support for dual-stack connectivity on Amazon Managed Grafana is available in all regions where the service is generally available. To get started, update your workspace configuration via the Amazon Managed Grafana console, API, or CLI. For more information, see the Amazon Managed Grafana User Guide. To learn more about best practices for configuring IPv6 in your environment, visit the whitepaper on IPv6 in AWS.

Amazon Inspector is now available in the AWS Asia Pacific (Taipei) Region

Today, AWS announces the availability of Amazon Inspector in the AWS Asia Pacific (Taipei) Region. Amazon Inspector is an automated vulnerability management service that continually scans AWS workloads including Amazon EC2 instances, container images, and AWS Lambda functions for software vulnerabilities and unintended network exposure across your AWS Organization. 

With this expansion, Amazon Inspector extends its security coverage to AWS Asia Pacific (Taipei) Region, designed to help customers automatically discover workloads, conduct continuous vulnerability assessments, and receive actionable security findings. The service is designed to detect newly launched Amazon EC2 instances, Lambda functions, and eligible container images pushed to Amazon Elastic Container Registry (ECR) and scan them for software vulnerabilities and unintended network exposure.

All accounts new to Amazon Inspector are eligible for a 15-day free trial to evaluate the service and estimate its cost. During the trial, all eligible Amazon EC2 instances, AWS Lambda functions, and container images pushed to Amazon ECR are continually scanned at no cost. After the trial period, you will be charged based on public pricing for Amazon Inspector. Visit the Amazon Inspector pricing page for more details.

To get started with Amazon Inspector visit our documentation or begin your free trial today.

Amazon ECS introduces pause and continue controls for service deployments

詳細を表示

Amazon Elastic Container Service (Amazon ECS) now enables you to pause service deployments at critical stages during deployment progression and continue deployments when ready. You can use these pause points to introduce manual decision points and interactive controls into your deployments for scenarios such as manual approval workflows, operational checks, integration tests, or custom automation, while continuing to use native Amazon ECS deployment strategies with managed traffic shifting, bake times, fast rollbacks, CloudWatch alarms, and deployment circuit breaker.

With this launch, you can configure a new PAUSE deployment lifecycle hook as part of your Amazon ECS service deployment configuration. When a deployment reaches a configured pause point, Amazon ECS pauses deployment progression and emits Amazon EventBridge events that you can use to trigger automation workflows, approval systems, or external validation processes. You can then continue or roll back the deployment using the new ContinueServiceDeployment API. With pause hooks, you can configure timeout durations up to 14 days and timeout actions to automatically continue or roll back the deployment if no action is received.

You can configure pause hooks for rolling, blue/green, linear, and canary deployment strategies using the Amazon ECS Console, AWS CLI, AWS SDKs, AWS CloudFormation, AWS CDK, and Terraform. You can use the ContinueServiceDeployment API through the Amazon ECS Console, AWS CLI, and AWS SDKs. This feature is available in all AWS commercial and AWS GovCloud (US) Regions. To learn more, see our documentation on pause hooks for service deployments and continuing service deployments.

Amazon MWAA now supports Apache Airflow 3.2

詳細を表示

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 3.2, the latest major release of the popular open-source workflow orchestration framework. Amazon MWAA is a managed service that lets you run Apache Airflow at scale without managing the underlying infrastructure. This release brings new data-aware scheduling capabilities and developer productivity improvements to teams building and operating data pipelines on AWS.

With Apache Airflow 3.2, you can now use asset partitioning to trigger downstream DAGs based on specific slices of data, such as a date-partitioned S3 path, rather than an entire asset, giving data engineering teams more precise control over pipeline execution. This release also expands Human-in-the-Loop (HITL) capabilities with a full audit history view for approvals, HITL support for the AgenticOperator, and synchronous callback support for Deadline Alerts. Additional improvements include Grid View virtualization for faster rendering of large DAGs, full XCom management from the Airflow UI, and async callable support in PythonOperator..

You can launch a new Apache Airflow 3.2 environment on Amazon MWAA, or upgrade from 2.11 or later, with just a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 3.2 visit the Amazon MWAA documentation, and the Apache Airflow 3.2 change log in the Apache Airflow documentation.

Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.

Amazon SageMaker HyperPod now supports data capture for inference workloads

Amazon SageMaker HyperPod now supports data capture for inference workloads, enabling customers to record inference request and response payloads for model monitoring, compliance, debugging, and offline analysis. Organizations deploying generative AI and machine learning models on HyperPod need systematic visibility into the inputs flowing into their models and the outputs returned to clients to detect model drift, satisfy regulatory audit requirements, debug production issues, and build ground-truth datasets for fine-tuning. Previously, customers had to either accept limited operational visibility into their inference workloads or build expensive custom logging pipelines outside the HyperPod Inference Operator.

With data capture, you can choose to record inference traffic at the SageMaker endpoint, at the load balancer, or at the model pod, depending on the level of visibility you need, and combine these options for layered observability. Captured data is delivered asynchronously to your Amazon S3 bucket and supports configurable sampling and encryption with customer-managed AWS KMS keys, so you can balance coverage with cost while keeping sensitive data protected. Data capture is designed to never block inference, ensuring production availability is preserved. You can enable data capture by configuring it on your inference endpoint when deploying models through the HyperPod Inference Operator or with SageMaker JumpStart.

This feature is available for SageMaker HyperPod clusters using the EKS orchestrator in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more, see Data capture for inference on HyperPod.

AWS Transfer Family web apps now support federated permissions with IAM Identity Center across AWS Regions

AWS Transfer Family web apps now support federated permissions with AWS IAM Identity Center across multiple AWS Regions. Previously, you could only create Transfer Family web apps in the Region where your IAM Identity Center instance was enabled. With IAM Identity Center's support for multi-Region replication, you can now replicate your identity configurations to another Region and create Transfer Family web apps there, reducing latency and improving reliability for your users.

After you enable a new Region in IAM Identity Center, you can create a Transfer Family web app in that Region. IAM Identity Center automatically replicates your workforce identities to the new Region, eliminating the need to reconfigure user credentials. Administrators can manage fine-grained permissions using the same workforce identities already configured in IAM Identity Center, and your Transfer Family web app users can sign in immediately using their existing credentials.

To get started, visit the Transfer Family User Guide. To enable IAM Identity Center across multiple Regions, refer to the IAM Identity Center User Guide. For regional availability, visit AWS Capabilities.

Announcing the general availability of a new AWS Local Zone in Istanbul, Türkiye

詳細を表示

Today, AWS announces the general availability of a new AWS Local Zone in Istanbul, Türkiye, bringing AWS infrastructure closer to end users, while enabling organizations to meet data residency requirements by storing and backing up data locally.

AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide.

The AWS Local Zone in Istanbul supports Amazon Elastic Compute Cloud (Amazon EC2) with C7i, M7i, and R7i instances, Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS with Local Snapshots and volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Virtual Private Cloud (Amazon VPC), AWS Direct Connect, and Application Load Balancer.  

To get started, enable the AWS Local Zone in Istanbul (eu-central-1-ist-1a) from the Zones tab in the Amazon EC2 console settings or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page. 

AWS Transform now modernizes networks during migrations

詳細を表示

Network migration teams previously spent days manually reviewing network designs and discovered conflicts only at deployment. AWS Transform now includes two new capabilities that solve both. A new modernization engine goes beyond network mapping to optimize constructs across naming, sizing, security, and structure while surfacing conflicts with existing VPCs already deployed in target accounts. It replaces days of manual review with instant guidance before a single resource provisions in AWS. The service also accepts network configuration files in any format, processing them for migration regardless of source tool or vendor.
Before provisioning begins, network teams review and act on modernization recommendations directly or edit any mapped VPC or subnet, retaining full control over the final network design. AWS Transform recommendations include:

  • Splitting VPCs with mixed workload tiers into segmented environments
  • Consolidating constructs fragmented by on-premises hardware constraints
  • Right-sizing CIDR allocations to eliminate waste and improve address space management
  • Standardizing naming conventions across all constructs
  • Flagging unrestricted security group rules that pose security risks
  • Removing out-of-scope resources from the target environment
  • Identifying CIDR conflicts between mapped and existing VPCs across target accounts, recommending resolutions, and implementing the customer's chosen path

Customers can now upload their network configuration files as-is, and AWS Transform translates them into AWS-compatible networks. Customers reach deployment faster, as they optimized their network, resolved conflicts, and made every decision themselves.

These features are available in all AWS Transform Target Regions.
To learn more, visit the AWS Transform product page and read the network migration user guide.

AWS Japan Blog

AWS Weekly Roundup: 1 周年を迎えた AWS Transform、Claude Platform on AWS、EC2 M3 Ultra Mac インスタンスなど (2026 年 5 月 18 日)

.NET、メインフレーム、および VMware ワークロード向けの AWS Transform がリリースされ […]

AWS Security Blog

Governing infrastructure as code using pattern-based policy as code

Organizations often struggle to enforce security and compliance requirements consistently across their cloud infrastructure. In one environment, a workload might be deployed in an AWS Region that was never approved for that class of data. In another, a security group might allow broader access than intended. Required tags might be missing. Encryption might be assumed […]

CIRT insights: How to help prevent unauthorized account removals from AWS Organizations

The AWS Customer Incident Response Team works with customers to help them recover from active security incidents. As part of this work, the team often uncovers new or trending tactics used by various threat actors that take advantage of specific customer configurations and designs. Understanding these tactics can help inform your architecture decisions, improve your […]

AWS Architecture Blog

How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances

This post introduces a video decoding optimization technique that we have ideated in collaboration with Synthesia Research Engineering team, which we call Asynchronous Frame Generation Pipeline. Adopting this technique allows you to overlap GPU compute, device-to-host (D2H) data transfer, and host-side post-processing. In this post, we apply this technique to the VAE decoder of a Wan video generation model as an example, where our benchmarks on G7e show increased GPU kernel utilization from 82% to 99.9%, in turn leading to an 8.2% decrease in latency (and increase in throughput) for video decoding. We expect this technique to benefit any customer with a chunked video generation pipeline that transfers frames to host memory.

How ALS GeoAnalytics LITHOLENS ™ revolutionizes core logging through machine learning with Amazon EKS

This post explores how ALS GeoAnalytics successfully deployed LITHOLENS ™ with Amazon Elastic Kubernetes Service (Amazon EKS) to scale model training and inference while minimizing cost.

AWS Machine Learning Blog

Implementing programmatic tool calling on Amazon Bedrock

In this post, we show three ways to implement Programmatic tool calling (PTC) on Amazon Bedrock: a self-hosted Docker sandbox on ECS for maximum control, a managed solution using Amazon Bedrock AgentCore Code Interpreter, and an Anthropic SDK-compatible path through a proxy for teams that prefer that developer experience.

Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository.

Extending conversational memory in Kiro CLI using Amazon Bedrock AgentCore Memory

In this post, we demonstrate how you can extend the conversational memory of Kiro CLI by implementing a custom Model Context Protocol (MCP) server that integrates with Amazon Bedrock AgentCore Memory. You can use Kiro CLI to interact with AI agents of Kiro directly from your terminal. Amazon Bedrock AgentCore Memory is a fully managed service that allows AI agents to retain information from past interactions, creating more intelligent and context-aware conversations. By implementing a custom MCP server, you can provide Kiro CLI with tools to store and retrieve conversation context, monitor memory usage, and manage the underlying Bedrock Agent Core Memory infrastructure.

Scalable voice agent design with Amazon Nova Sonic: multi-agent, tools, and session segmentation

In this post, you’ll learn how to use Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands BidiAgent to build scalable, maintainable voice agents that handle these challenges efficiently, resulting in more responsive and intelligent customer interactions. We’ll explore three popular architectural patterns for voice agents, highlighting their trade-offs and best practices for minimizing latency.
前日
Archive
翌日