About This Architecture

Trashure Youth Funding System uses a multi-tier AWS architecture with WAF, CDN, and ALB to route traffic across dual availability zones, isolating presentation, application, and data layers in separate VPCs and subnets. Youth entrepreneurs and administrators authenticate through API Gateway, triggering Auth-Profile Manager, which feeds the Predictive Analytics Engine running TensorFlow and Scikit-Learn models for funding recommendations. PostgreSQL primary-standby replication and Pinecone/Redis vector databases ensure high availability and low-latency ML inference, while Web Scraper Service ingests external funding portal data via message queues into the Feature Store and Model Registry. This architecture demonstrates best practices for ML-driven fintech platforms: security-first design with WAF protection, geographic redundancy across AZ-1 and AZ-2, and separation of concerns enabling independent scaling of compute, cache, and analytics workloads. Fork this diagram on Diagrams.so to customize subnets, add additional AZs, or integrate alternative ML frameworks and vector stores. Consider adding VPC Flow Logs and GuardDuty for enhanced security monitoring in production deployments.

People also ask

How do you design a scalable AWS architecture for an ML-driven funding recommendation platform with high availability?

Trashure's architecture uses dual availability zones with separate public and private subnets for presentation, application, and data layers. WAF and CDN protect user traffic, API Gateway routes requests to Auth-Profile Manager, which triggers the Predictive Analytics Engine running TensorFlow models. PostgreSQL primary-standby and Pinecone/Redis replicas ensure failover and low-latency inference