AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach.
Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow.
Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in US East (N. Virginia) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.
Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.
You can start using Amazon EC2 G7 instances today in three AWS Regions: US East (N. Virginia and Ohio), and US West (Oregon). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.
Amazon Elastic Kubernetes Service (Amazon EKS) now supports Amazon Application Recovery Controller (ARC) zonal shift and autoshift for clusters using EKS Auto Mode. ARC helps you manage and coordinate recovery across AWS Regions and Availability Zones (AZs). With this launch, EKS Auto Mode automatically protects your compute during a zonal shift at no additional cost or configuration, helping you maintain Kubernetes application availability by shifting in-cluster network traffic away from an impaired AZ.
Customers run highly available applications across multiple AZs in Amazon EKS to eliminate a single point of failure. Because EKS Auto Mode manages compute on your behalf, you get zonal shift support without setting flags, granting permissions, or managing Karpenter versions; simply enable ARC zonal shift on your cluster. When a zonal shift is activated, EKS Auto Mode stops provisioning new capacity in the impaired AZ and halts voluntary disruptions such as consolidation and drift for nodes in that zone. It also prevents voluntary disruptions in healthy zones if they depend on scheduling pods to the impaired zone. When the shift expires or is canceled, normal operations resume. You can start a shift manually, or authorize AWS to manage it using zonal autoshift, with practice runs to verify your cluster functions with one less AZ.
ARC zonal shift support for EKS Auto Mode is available in all AWS Regions where EKS Auto Mode is offered. To learn more, visit the Amazon EKS product page, the ARC zonal shift documentation, and the Amazon EKS Auto Mode documentation.
Amazon SageMaker HyperPod now supports AMI-based configuration for Slurm clusters that use continuous provisioning. Continuous provisioning adds nodes to the cluster as capacity becomes available, and this launch extends AMI-based configuration to clusters using this mode. With this support, clusters using continuous provisioning can be created without downloading, configuring, or uploading lifecycle configuration scripts to Amazon S3.
AMI-based configuration provisions nodes with the software and configurations needed for a production-ready environment to run AI/ML training workloads, including required software such as Docker, Enroot, and Pyxis, and configurations such as Slurm accounting, SSH key generation, and log rotation. When using continuous provisioning, each node is configured from the AMI as it is added to the cluster, without the need to manage lifecycle configuration scripts, so nodes become available to schedule jobs sooner. To enable AMI-based configuration, omit the LifeCycleConfig block from the instance group configuration when creating clusters via the API, or select "None" under Lifecycle scripts in Custom setup when using the SageMaker AI console. For additional customization on top of the AMI-based configuration baseline, an extension script can be provided by specifying the OnInitComplete parameter and SourceS3Uri in the LifeCycleConfig block via the API, or by providing the S3 URI in the "Extension script file in S3" field in Custom setup when using the console. Custom lifecycle configuration scripts remain fully supported for use cases that require full control over provisioning.
AMI-based node lifecycle configuration for Slurm clusters using continuous provisioning is available in all AWS Regions where SageMaker HyperPod is available. To get started, see Getting started with SageMaker HyperPod using the AWS CLI or Getting started with SageMaker HyperPod using the SageMaker AI console in the SageMaker user guide.
Today, Amazon Location Service announced new enhancements to its Places APIs that give developers greater control over address name formatting, multilingual address, travel-optimized POI search, and drive-through data. These capabilities span the Geocode, ReverseGeocode, GetPlace, Suggest, Autocomplete, SearchNearby, and SearchText APIs. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing.
Developers can now control how address component names are returned using the new AddressNamesMode parameter — choosing between matched (echoing input), normalized (canonical names), or administrative (government hierarchy names) — and override behavior per component with AddressNamesVariant. The new AddressTranslations parameter returns place name translations in 50+ languages, making it easier to build multilingual applications. A TravelMode parameter optimizes Suggest and SearchText results for users on the move, improving relevance for navigation and in-vehicle scenarios. Additionally, GetPlace, Suggest, SearchNearby, and SearchText now return a DriveThrough attribute indicating whether a place offers drive-through service — useful for logistics, food delivery, and navigation applications. The Geocode API also now supports a new Parsing.AdditionalInfo response field with additional detail about how the input address was interpreted.
Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West).
To get started, see the Amazon Location Service Places API reference, or learn more about Places in the Amazon Location Service Developer Guide.
Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data.
The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a "Troubleshoot with AI" option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail.
The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8in, R8ib, R8idn, and R8idb instances are available in the AWS Asia Pacific (Tokyo) and Europe (Frankfurt, Ireland) regions. These instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS and feature the latest sixth generation AWS Nitro cards. These instances deliver up to 43% better compute performance per vCPU compared to previous generation R6in and R6idn instances.
R8in, R8idn instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances. R8in instances are ideal for workloads such as real-time big data analytics, distributed web scale in-memory caches, caching fleets for AI/ML clusters, and Telco applications such as 5G User Plane Function (UPF). R8idn instances are ideal for network-intensive general purpose workloads requiring local storage, such as distributed compute, data analytics, and high-performance file systems.
R8ib, R8idb instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances. R8ib instances are best suited for workloads that benefit from high block storage performance, such as high-performance file systems and NoSQL databases. R8idb instances are ideal for storage-intensive general purpose workloads such as large commercial databases, data lakes, and NoSQL databases that benefit from both high EBS throughput and low-latency local NVMe storage.
R8in, R8ib, R8idn, and R8idb instances support Elastic Fabric Adapter (EFA) networking on 48xlarge, 96xlarge, metal-48xl, and metal-96xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.
Amazon EC2 R8in an R8ib instances are available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain, Frankfurt, Ireland) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 R8i instance page.
2026 年 7 月 1 日より、Claude Sonnet 5 が Kiro の IDE、CLI、Web でご利用いただけるようになりました。Sonnet 5 は Anthropic がこれまでに提供した中で最もエージェント性能に優れた Sonnet モデルであり、Sonnet 4.6 から大幅にアップグレードされ、推論力・ツール利用・コーディング能力が強化されています。しかも価格は Sonnet クラスのままです。
2026 年 7 月 1 日より、Claude Sonnet 5 が Kiro の IDE、CLI、Web でご利用いただけるようになりました。Sonnet 5 は Anthropic がこれまでに提供した中で最もエージェント性能に優れた Sonnet モデルであり、Sonnet 4.6 から大幅にアップグレードされ、推論力・ツール利用・コーディング能力が強化されています。しかも価格は Sonnet クラスのままです。
Amazon Web Services EMEA Sarl (AWS) has been designated as a critical third party (CTP) to the UK financial sector by HM Treasury. The CTP regime came into force on January 1, 2025, and establishes a framework through which the Bank of England, PRA, and FCA (collectively the UK regulators) can set requirements on and […]
In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator.
Evolving from a traditional software as a service (SaaS) platform into a next-generation agentic AI platform meant orchestrating multiple specialized agents across long-running enterprise programs. Each agent operates with persistent context, secure tool access, and production-grade reliability. We built that system on Amazon Bedrock AgentCore using the Strands Agents SDK. This post walks through how we architected it, which agents we built, and the outcomes for our customers.
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure. The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct instance access, Amazon SageMaker AI inference endpoints for managed serving, and Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) when inference needs to fit into an existing container framework. You also learn operational practices for production deployments.
In this post, we show you how to combine case management with agentic automation capabilities in Quick Automate. We introduce case management and explore the lifecycle of cases in an agentic workflow from case creation through processing to resolution. We cover how to create and manage single or multiple cases, automatically track and update status, handle exceptions, and incorporate Human-in-the-loop (HITL) steps within workflows. We also show the case creator-processor pattern that enables dynamic scaling. Finally, we walk through how to structure case management for enterprise processes, including HITL and case tracking, through a real-life use case.
In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without extract, transform, and load (ETL). The same Stardog deployment works behind AWS computes (Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS Lambda). We use AgentCore here because it bundles inbound auth, hosting, and tool credentials into one managed service.
This post describes how Henry Schein One closed that gap by building Image Verify, an AI-powered quality verification system on Amazon SageMaker AI that evaluates dental X-ray quality at the point of capture, in real time, across thousands of locations. The system went from concept to over 10,000 active locations within months and has already processed over 11 million X-rays and growing at 1.5 million per week. Henry Schein One is now scaling toward 40,000 locations globally across four regions.
In this post, we explore what makes the Nemotron 3 architecture unique, walk through the fine-tuning techniques available, and show you step-by-step how to get started with serverless customization using SageMaker Studio.
We recently announced the launch of AWS Lambda MicroVMs, a new serverless compute primitive that provides VM-level isolation, near-instant startup performance, and state retention. You can now give each user or job their own execution environment to securely run just-in-time code – either user or AI generated – without managing virtualization infrastructure or choosing between […]
AWS Outposts extend AWS infrastructure, services, APIs, and tools to on-premises locations for workloads that require low latency, local data processing, or data residency. In this post, you learn how to configure instances running on an Outpost to support the required IOPS and throughput for your application. The actual IOPS available to an instance is […]