# Technical How-to Published articles for Technical How-to. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Architecting a secure landing zone in the AWS European Sovereign Cloud DevFeed: [Architecting a secure landing zone in the AWS European Sovereign Cloud]() Original publisher: [Read original article]() Author: Pablo Pagani Published: 2026-09-16T21:20:48Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Cloud](), [Architecture & Design](), [AWS IAM](), [Infrastructure as code](), [CI/CD](), [SIEM, Security](), [Incident response]() Tags: [advanced-300](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [cloud](), [eu](), [europe](), [incident-response](), [infrastructure-as-code-iac](), [partition](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to architect a secure, scalable landing zone in the AWS European Sovereign Cloud. It describes the aws-eusc partition boundary and covers governance, identity, logging, data protection, network design, CI/CD, artifact distribution, and incident response. ### Source excerpt The AWS European Sovereign Cloud is a new, independent cloud for Europe, physically and logically separate from existing AWS Regions and operated within the European Union (EU). It provides the same services, features, and APIs as AWS commercial Regions, but runs as a distinct AWS partition (aws-eusc), with its own control plane, AWS Identity and [...] ## Fault tolerant distributed training on Amazon EKS using NVRx DevFeed: [Fault tolerant distributed training on Amazon EKS using NVRx]() Original publisher: [Read original article]() Author: Aravind Neelakantan Published: 2026-09-16T18:59:25Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [NCCL](), [Amazon Elastic Kubernetes Service](), [PyTorch](), [distributed-training](), [Resilience](), [GPU]() Tags: [advanced-300](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [async](), [benchmarks](), [distributed-training](), [gpu](), [pytorch](), [resiliency](), [technical-how-to]() ### AI overview This tutorial integrates NVIDIA Resiliency Extension (NVRx) with PyTorch FSDP training on Amazon EKS. It covers asynchronous checkpointing, in-process restart, and in-job restart, and reports H100 benchmarks at 2- to 8-node scale with 99%+ training efficiency and recovery measured in seconds. ### Source excerpt Integrate NVIDIA Resiliency Extension (NVRx) into PyTorch FSDP training on Amazon EKS to overlap checkpoint I/O with training and recover from GPU faults in seconds. This post covers async checkpointing, in-process restart, and ft_launcher in-job restart, with H100 benchmarks at 2 to 8 nodes showing 99%+ training efficiency and second-scale recovery. ## Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation DevFeed: [Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation]() Original publisher: [Read original article]() Author: Samantha Stuart Published: 2026-09-16T15:17:37Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [pii](), [Serverless](), [AWS Lambda](), [AWS Step Functions](), [Artificial Intelligence](), [Amazon Web Services](), [Ansible]() Tags: [advanced-300](), [amazon-bedrock](), [amazon-bedrock-data-automation](), [aws](), [aws-lambda](), [aws-step-functions](), [how-to](), [pii-redaction](), [pipeline](), [precision](), [serverless](), [technical-how-to]() ### AI overview This tutorial presents a serverless AWS pipeline for detecting and redacting personally identifiable information in scanned documents and images. It uses Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda, with a token-matching quality check to improve recall on degraded and handwritten documents. ### Source excerpt Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ## AWS STS simplifies session token size limits and adds session token size monitoring DevFeed: [AWS STS simplifies session token size limits and adds session token size monitoring]() Original publisher: [Read original article]() Author: Rishi Tripathy Published: 2026-09-15T22:21:59Z Content type: release Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Security](), [Monitoring](), [Amazon CloudWatch](), [AWS CloudTrail](), [API]() Tags: [advanced-300](), [amazon-cloudwatch](), [apis](), [aws](), [aws-cloudtrail](), [aws-security-token-service](), [aws-sts](), [infrastructure](), [metrics](), [monitoring](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to](), [token]() ### AI overview AWS Security Token Service has replaced its separate packed policy and overall session token limits with a single 4,096-byte limit. It now reports session token size and limit utilization in API responses, Amazon CloudWatch metrics, and AWS CloudTrail events. ### Source excerpt AWS Security Token Service (AWS STS) has simplified session token size limits, giving you more room for your session policies and session tags. STS has replaced the packed policy size and the overall session token size limits with a single token size limit of 4,096 bytes. STS now reports session token size in API responses, [...] ## Architecting resilient authentication with Amazon Cognito multi-Region replication DevFeed: [Architecting resilient authentication with Amazon Cognito multi-Region replication]() Original publisher: [Read original article]() Author: Abrom Douglas Published: 2026-09-15T19:00:52Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [Amazon Cognito](), [Replication](), [Authentication](), [identity and access management](), [Amazon Web Services](), [real-time](), [JSON Web Tokens]() Tags: [advanced-300](), [amazon-cognito](), [authentication](), [aws](), [failover](), [identity-and-access-management](), [real-time](), [replication](), [security-blog](), [security-identity-compliance](), [technical-how-to](), [token]() ### AI overview This AWS article explains how Amazon Cognito multi-Region replication supports resilient authentication by replicating user pools across AWS Regions, with eventual consistency, failover, and interoperable sessions and JSON web tokens. It also covers preparation, architecture decisions, and failover strategies for B2C, B2B, and M2M use cases. ### Source excerpt Your consumer identity and access management (CIAM) system is the foundation of your customer experience. It's how users sign in, access services, and engage with your applications. As your business scales across geographies, ensuring authentication is always available becomes a core architectural requirement. However, building multi-Region authentication has traditionally required complex custom replication solutions that [...] ## Optimizing cost and latency with Amazon Bedrock prompt caching DevFeed: [Optimizing cost and latency with Amazon Bedrock prompt caching]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-09-15T16:18:19Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Caching](), [Latency](), [API](), [foundation-models](), [LangChain](), [Multi-tenancy]() Tags: [advanced-300](), [amazon-bedrock](), [api](), [caching](), [foundation-models](), [integration](), [langchain](), [latency](), [pricing](), [technical-how-to](), [tokens]() ### AI overview This AWS post explains how Amazon Bedrock prompt caching can reduce repeated input-token costs by up to 90 percent and lower time to first token when requests reuse the same context. It presents six scenarios using the Converse API, including document, system prompt, tool definition, mixed TTL, tenant-isolated, and LangChain caching. ### Source excerpt Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration. ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## Operationalizing least privilege: Automate IAM remediation through your CI/CD pipeline DevFeed: [Operationalizing least privilege: Automate IAM remediation through your CI/CD pipeline]() Original publisher: [Read original article]() Author: Luis Pastor Published: 2026-09-15T15:53:51Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AWS Identity and Access Management (IAM)](), [CI/CD](), [Infrastructure as code](), [AWS CloudFormation](), [Amazon Web Services]() Tags: [aws](), [aws-cloudformation](), [aws-cloudtrail](), [aws-iam](), [aws-identity-and-access-management-iam](), [aws-organizations](), [best-practices](), [ci-cd](), [expert-400](), [iam](), [infrastructure-as-code-iac](), [least-privilege](), [pull-request](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article describes an automated workflow for operationalizing least privilege in AWS. It explains how AWS IAM Access Analyzer identifies unused permissions and how remediation can be routed through different paths for infrastructure-as-code-managed, manually created, and unused IAM principals. ### Source excerpt The principle of least privilege is straightforward to articulate but challenging to maintain at scale. When teams first deploy applications to AWS, they often grant broader permissions than strictly necessary; it's faster to get things working, and the plan is always to tighten permissions later. But later rarely comes. Permissions accumulate, AWS Identity and Access [...] ## Validating multi-agent decisions with Step Functions and Bedrock AgentCore DevFeed: [Validating multi-agent decisions with Step Functions and Bedrock AgentCore]() Original publisher: [Read original article]() Author: Ben Freiberg Published: 2026-09-14T16:47:23Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Amazon Bedrock AgentCore](), [AWS Step Functions](), [Artificial Intelligence](), [Generative AI](), [Automation]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [audit-trail](), [automation](), [aws-step-functions](), [generative-ai](), [regulatory](), [technical-how-to](), [workflow]() ### AI overview The article presents a pattern for validating multi-agent airline rebooking decisions. Amazon Bedrock AgentCore agents propose options, while AWS Step Functions applies deterministic validation, supports large-scale parallel processing, enables human review, and maintains an execution history for auditing. ### Source excerpt Orchestrating specialized Amazon Bedrock AgentCore agents with AWS Step Functions gives you the reasoning power of generative AI with the guardrails of deterministic validation. Agents propose options, and deterministic code validates them before any action is taken, demonstrated here with an airline rebooking workflow. ## Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2 DevFeed: [Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:16Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [Amazon CloudWatch](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Database](), [Amazon EC2](), [Architecture & Design](), [Amazon Web Services](), [AWS CloudFormation]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [aws](), [aws-cloudformation](), [concurrency](), [monitoring](), [performance](), [postgresql](), [rds-for-postgresql](), [simulation](), [technical-how-to]() ### AI overview This tutorial explains how to diagnose and resolve lock contention in Amazon Aurora PostgreSQL using Amazon CloudWatch Database Insights. It demonstrates Lock Analysis and the Lock Tree visualization for identifying blocking sessions, then covers immediate fixes, configuration changes, optimistic concurrency control, asynchronous processing, SKIP LOCKED, and row splitting. ### Source excerpt Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput. ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload DevFeed: [Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload]() Original publisher: [Read original article]() Author: Nick McCarthy Published: 2026-09-11T18:24:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [AI Bots](), [ai observability]() Tags: [advanced-300](), [agent](), [agentic](), [amazon-bedrock](), [api](), [benchmarking](), [cost](), [evaluation](), [generative-ai](), [open-source](), [openai](), [technical-how-to]() ### AI overview The article presents an open-source benchmark for comparing OpenAI models on Amazon Bedrock with OpenAI API baselines by cost per correct answer, multi-turn agent trajectory cost, and deliverable quality. ### Source excerpt Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock. ## Build interactive MCP Apps using Amazon Bedrock AgentCore DevFeed: [Build interactive MCP Apps using Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Dantis Stephen Published: 2026-09-11T18:23:17Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [Web Development]() Tags: [amazon-bedrock-agentcore](), [chatgpt](), [claude](), [how-to](), [html](), [intermediate-200](), [mcp](), [model-context-protocol](), [serverless](), [technical-how-to](), [ui]() ### AI overview Tutorial on building and deploying an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore for use across supported AI hosts. ### Source excerpt Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension. ## Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL DevFeed: [Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-11T14:53:57Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon S3](), [Apache Iceberg](), [AWS Lambda](), [AWS IAM](), [Latency]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-s3-tables](), [analytics](), [apache-iceberg](), [aws-lambda](), [duckdb](), [how-to](), [iam](), [integration](), [sql](), [technical-how-to]() ### AI overview This tutorial explains how to run ad hoc SQL analytics on Amazon DynamoDB data with DuckDB through a zero-ETL replication flow. ### Source excerpt Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL. ## Building resilient real-time streaming workers with Amazon DynamoDB leases DevFeed: [Building resilient real-time streaming workers with Amazon DynamoDB leases]() Original publisher: [Read original article]() Author: Siddhesh Tiwari Published: 2026-09-10T16:14:22Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [WebSocket](), [Amazon EC2](), [Amazon Elastic Kubernetes Service]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-ec2](), [amazon-eks](), [amazon-elastic-container-service](), [aws-fargate](), [how-to](), [real-time](), [streaming](), [technical-how-to](), [workers]() ### AI overview A tutorial for building resilient real-time WebSocket workers with Amazon DynamoDB leases. It covers conditional-write ownership, orphan reconciliation for automatic failover, and graceful shutdown to reduce deployment downtime on Amazon ECS and AWS Fargate. ### Source excerpt Real-time streaming workers that hold hundreds of persistent WebSocket connections lose data when a worker fails. Learn how to build a WebSocket fleet management system on Amazon ECS and AWS Fargate that uses Amazon DynamoDB conditional writes as a distributed lease to track ownership, fail over automatically, and deploy with low downtime. ## Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate DevFeed: [Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate]() Original publisher: [Read original article]() Author: Chaytanya Kumar Published: 2026-09-10T16:08:57Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Automation](), [Amazon S3](), [CSV](), [Amazon Web Services](), [datasets]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-simple-storage-service-s3](), [automation](), [aws](), [data](), [how-to](), [technical-how-to](), [tutorial]() ### AI overview A tutorial for automating RFI questionnaire processing with Amazon Quick Automate. It reads multi-tab workbooks from Amazon S3, extracts and structures questions, and writes CSV output to Amazon S3. ### Source excerpt Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 -- cutting development from days to hours. ## Model-agnostic PII detection with LLMs DevFeed: [Model-agnostic PII detection with LLMs]() Original publisher: [Read original article]() Author: Christophe Dupuy Published: 2026-09-10T16:02:16Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat]() Tags: [advanced-300](), [amazon-bedrock](), [benchmark](), [fine-tuning](), [inference](), [large-language-models-llms](), [llms](), [pii](), [security](), [technical-how-to](), [vpc]() ### AI overview A tutorial on building a configurable, model-agnostic PII detector with large language models. It explains prompt-based entity configuration, implementation, benchmarking, and running the detector on custom data. ### Source excerpt A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors. ## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM]() Original publisher: [Read original article]() Author: Dmitry Soldatkin Published: 2026-09-09T22:26:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Deployment](), [Inference](), [Machine learning](), [Language models](), [GPU](), [long-context](), [Mixture of Experts (MoE)]() Tags: [advanced-300](), [agentic](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [deployment](), [gpu](), [how-to](), [inference](), [long-context](), [moe](), [nvfp4](), [quantization](), [qwen](), [reasoning](), [technical-how-to](), [tool](), [vllm]() ### AI overview A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding. ### Source excerpt Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding. ## Testing application resilience with Amazon SQS and AWS Fault Injection Service DevFeed: [Testing application resilience with Amazon SQS and AWS Fault Injection Service]() Original publisher: [Read original article]() Author: Richard Whitworth Published: 2026-09-09T21:33:59Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Simple Queue Service (SQS)](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [AWS IAM](), [AWS Identity and Access Management (IAM)]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-simple-queue-service-sqs](), [amazon-sqs](), [automation](), [aws](), [aws-fault-injection-service-fis](), [experiments](), [how-to](), [iam](), [observability](), [resilience](), [technical-how-to](), [testing]() ### AI overview A tutorial on testing application resilience when Amazon SQS data-plane operations fail. It uses AWS Fault Injection Service and Systems Manager Automation to progressively deny queue access, evaluate recovery and observability with CloudWatch metrics, and avoid IAM deny-policy lockouts. ### Source excerpt Learn how to use AWS Fault Injection Service and AWS Systems Manager Automation to run progressive chaos experiments against Amazon SQS queues. Validate that your retry logic, circuit breakers, and dead-letter queues actually work under failure before a real outage hits production. ## Validating multi-Region DR for Terraform Enterprise with AWS FIS DevFeed: [Validating multi-Region DR for Terraform Enterprise with AWS FIS]() Original publisher: [Read original article]() Author: Frenil Randeria Published: 2026-09-09T21:05:02Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Terraform](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [Amazon S3](), [Amazon EC2](), [Databases]() Tags: [advanced-300](), [amazon-aurora](), [amazon-ec2](), [amazon-s3](), [aws](), [aws-fault-injection-service-fis](), [disaster-recovery](), [postgresql](), [technical-how-to](), [terraform](), [validation]() ### AI overview This article explains how to validate customer-operated multi-Region disaster recovery for Terraform Enterprise on AWS using three-phase AWS FIS experiments. It covers failover and failback testing, hidden dependency discovery, and reported recovery times of 12-14 minutes. ### Source excerpt Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid. ## Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers DevFeed: [Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers]() Original publisher: [Read original article]() Author: Ananth Raghavendra Published: 2026-09-09T15:51:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [Containers](), [vlm](), [GPU](), [PyTorch]() Tags: [amazon-eks](), [containers](), [gpu](), [inference](), [intermediate-200](), [pytorch](), [technical-how-to](), [vulnerabilities]() ### AI overview The article introduces AWS Ray Serve Deep Learning Containers as a maintained, pre-tested inference option for teams moving from unmaintained TorchServe workloads. It demonstrates deploying a vision-language model on Amazon EKS using a single GPU node. ### Source excerpt TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. ## Automate user-level custom permissions for Amazon Quick DevFeed: [Automate user-level custom permissions for Amazon Quick]() Original publisher: [Read original article]() Author: Ashok Dasineni Published: 2026-09-09T15:45:24Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Authorization](), [AWS Lambda](), [IAM Identity Center](), [Script]() Tags: [amazon-eventbridge](), [amazon-quick-suite](), [api](), [automation](), [aws-iam-identity-center](), [aws-lambda](), [batch](), [cli](), [intermediate-200](), [python](), [technical-how-to]() ### AI overview This tutorial presents four ways to automate user-level custom permissions in Amazon Quick: setting permissions during user registration, applying account or role defaults, using EventBridge and Lambda for group-based logic, and running retroactive batch updates. ### Source excerpt Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script. ## Building async Python applications with Tortoise ORM and Amazon Aurora DSQL DevFeed: [Building async Python applications with Tortoise ORM and Amazon Aurora DSQL]() Original publisher: [Read original article]() Author: Lasita Bhattacharya Published: 2026-09-09T15:27:43Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [Concurrency](), [AWS IAM](), [Database](), [CRUD]() Tags: [advanced-300](), [amazon-aurora](), [concurrency](), [database](), [dsql](), [how-to](), [iam](), [orm](), [postgresql](), [python](), [rideshare](), [serverless](), [technical-how-to]() ### AI overview A tutorial for building a high-concurrency asynchronous Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. It covers UUID-based models, IAM-authenticated asyncpg connections, OCC retry handling, and asynchronous CRUD operations. ### Source excerpt Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic. [Next page]()