About This Architecture

AI Life Scenario Simulator on AWS combines a three-tier architecture across two availability zones with a dedicated AI/ML core for real-time simulation and NLP processing. Traffic flows from mobile and web users through CloudFront CDN, WAF, and ALB into microservices (ECS), Lambda functions, and EKS-based simulation engines distributed across public and private subnets. The AI/ML layer leverages SageMaker, Bedrock, Comprehend, and EMR for model training, feature engineering, and natural language understanding, while the data layer persists results in RDS Aurora, DynamoDB, Redshift, and S3 with ElastiCache acceleration. This architecture demonstrates high-availability patterns, security best practices (Shield Advanced, KMS encryption, GuardDuels), and event-driven workflows via Step Functions and SNS for scenario insights. Fork this diagram on Diagrams.so to customize subnets, add additional AZs, or swap compute services for your simulation workload. The multi-layer approach isolates compute, AI/ML, and data tiers to optimize cost and blast radius while supporting real-time and batch analytics via Athena and QuickSight.

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How do you architect a scalable AWS platform for real-time AI scenario simulation with high availability and security?

This diagram shows a three-tier AWS architecture spanning two AZs with CloudFront/WAF/ALB for ingress, ECS microservices and EKS simulation engines in private app subnets, and a dedicated AI/ML tier running SageMaker, Bedrock, Comprehend, and EMR. Data persists in RDS Aurora (primary/standby), DynamoDB, Redshift, and S3, with KMS encryption, GuardDuty monitoring, and Step Functions orchestrating e