# Advanced (300) Published articles for Advanced (300). 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. ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## 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. ## How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock DevFeed: [How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock]() Original publisher: [Read original article]() Author: Shawn Kelly Published: 2026-09-14T15:58:57Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Artificial Intelligence](), [API](), [Architecture & Design](), [Security](), [Finance](), [Amazon Web Services](), [data]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [customer-solutions](), [fintech](), [onboarding](), [pci-dss](), [security](), [soc-2]() ### AI overview The article describes how Ninth Wave built Compass, a multi-agent AI onboarding assistant powered by Amazon Bedrock AgentCore. Compass helps financial institutions validate bank APIs, map fields to Financial Data Exchange standards, and score production readiness while supporting secure, compliant open finance connectivity. ### Source excerpt Learn how Ninth Wave built Compass, a multi-agent AI onboarding assistant on Amazon Bedrock AgentCore that validates bank APIs against Financial Data Exchange (FDX) standards, scores compliance, and compresses open finance onboarding from weeks to minutes while meeting SOC 2 and PCI DSS requirements. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## 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. ## Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations DevFeed: [Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations]() Original publisher: [Read original article]() Author: Meghana Ashok Published: 2026-09-11T18:26:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [ai observability](), [AWS IAM](), [incident](), [Orchestration](), [telemetry]() Tags: [advanced-300](), [agent](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [aws-identity-and-access-management-iam](), [best-practices](), [devops](), [incident](), [infrastructure-monitoring](), [logs](), [metrics](), [monitoring](), [orchestration](), [production](), [tracing]() ### AI overview The article describes monitoring production multi-agent systems with Amazon Bedrock AgentCore Evaluations for continuous quality assessment and AWS DevOps Agent for autonomous infrastructure incident investigation. ### Source excerpt Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system. ## 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. ## 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. ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference DevFeed: [Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:58:09Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Large Language Model](), [Low-Latency Inference](), [Caching](), [Inference](), [Inference Performance]() Tags: [advanced-300](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [benchmarks](), [caching](), [inference](), [latency](), [llama](), [llm](), [routing](), [tensorrt-llm](), [vllm]() ### AI overview Amazon SageMaker Inference introduces prefix-aware routing for LLM requests. By consistently sending requests with matching prompt prefixes to the same instance, it improves reuse of cached KV computations and can reduce time to first token. ### Source excerpt Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%. ## 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. ## Architecting SASE solutions using AWS Local Zones DevFeed: [Architecting SASE solutions using AWS Local Zones]() Original publisher: [Read original article]() Author: Lakshmi VP Published: 2026-09-09T20:36:09Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [networking](), [Network Services]() Tags: [advanced-300](), [architecture](), [aws](), [aws-local-zones](), [aws-marketplace](), [best-practices](), [edge](), [latency](), [routing](), [sase](), [security](), [zero-trust]() ### AI overview The article explains how AWS Local Zones can support SASE deployments by placing virtual security appliances closer to distributed users to reduce latency while maintaining security controls. ### Source excerpt Organizations with geographically distributed workforces face a trade-off between security and low-latency access. This post explores how to use AWS Local Zones and Secure Access Service Edge (SASE) solutions to deploy virtual security appliances closer to end users, covering key design principles, capacity planning, and traffic routing. [Next page]()