About This Architecture

Real-time visual inspection MLOps architecture on AWS separates production inference from ML development across two VPCs with auto-scaling SageMaker endpoints. External API clients submit images through CloudFront and API Gateway into a DMZ public subnet, where SQS queues trigger Lambda functions for quality checks before routing to SageMaker Real-Time Endpoints in a private inference subnet with 5-15 instance auto-scaling. Results flow through Lambda formatters to DynamoDB and S3, while CloudWatch, X-Ray, and QuickSight provide drift monitoring and performance dashboards across dedicated monitoring subnets. A separate Development VPC hosts SageMaker Notebooks, Training Jobs, Feature Store, Model Registry, and a complete CI/CD pipeline using CodePipeline, CodeBuild, and Step Functions for automated model deployment. Fork this architecture on Diagrams.so to customize subnet CIDR ranges, adjust SageMaker instance types, or add your own preprocessing Lambda functions for manufacturing quality control workflows.

People also ask

How do I architect a production MLOps pipeline on AWS for real-time visual inspection with auto-scaling and drift monitoring?

Deploy a dual-VPC architecture separating production inference (SageMaker Real-Time Endpoints with 5-15 instance auto-scaling, Lambda preprocessing, SQS queuing) from ML development (SageMaker Notebooks, Training Jobs, Feature Store, Model Registry). Use CodePipeline and Step Functions for CI/CD, CloudWatch and X-Ray for drift monitoring, and private subnets for security isolation.