About This Architecture

Multi-AZ Streamlit ML inference app deployed on AWS ECS Fargate with high availability across us-east-1a and us-east-1b. Developers push container images to Amazon ECR, which triggers ECS Fargate services in public subnets with assigned public IPs and inbound TCP 8501 security group rules. Users access the Streamlit app through the Internet Gateway, while both Fargate tasks stream application logs to CloudWatch under /ecs/mlops-heart-disease for monitoring and debugging. This architecture demonstrates containerized ML model serving with built-in observability, eliminating server management overhead while maintaining fault tolerance across availability zones. Fork this diagram on Diagrams.so to customize VPC CIDR blocks, add private subnets with NAT gateways, or integrate an Application Load Balancer for production workloads.

People also ask

How do I deploy a Streamlit machine learning app on AWS ECS Fargate with high availability and logging?

This diagram shows a production-ready setup using ECS Fargate services across two availability zones (us-east-1a and us-east-1b) in a VPC, with developers pushing container images to Amazon ECR and users accessing the Streamlit app on port 8501 through the Internet Gateway. Both Fargate tasks automatically log to CloudWatch under /ecs/mlops-heart-disease, enabling centralized monitoring without ma