AWS
AI & ML
intermediate
SageMaker Train & Serve — AWS Architecture Template
SageMaker trains on S3 datasets, publishes model artifacts, and serves real-time inference behind API Gateway.
Why this architecture works
- Versioned model artifacts in S3 make every deployment reproducible and instantly roll-backable
- A managed SageMaker endpoint autoscales inference and supports blue/green model swaps
- The Lambda proxy validates and shapes requests, keeping the ML endpoint off the public internet
- A scoped training role restricts jobs to the exact data prefixes they should read
- Endpoint latency and invocation-error metrics in CloudWatch catch model or capacity drift early
What's inside (8 resources)
Training Data
aws-s3
Inference API
aws-api-gateway
Training Jobs
aws-sagemaker
Inference Proxy
aws-lambda
Model Artifacts
aws-s3
Training Role
aws-iam
Inference Endpoint
aws-sagemaker
Model Metrics
aws-cloudwatch
From template to running infrastructure
- Open this template in the CloudForge visual designer (free account, no credit card).
- Customize resources, names, and connections on the drag-and-drop canvas — or ask Vani, the AI architect, to adapt it.
- Generate production-ready Terraform or CloudFormation in one click.
- Review the plan diff and security scan, then deploy with human approval.