About This Architecture

Production-grade MLOps architecture for real-time visual inspection using SageMaker endpoints with auto-scaling (5-15 instances) behind API Gateway and WAF. Image quality checks via Lambda validate inputs from S3 before orchestrating inference, with SQS buffering requests and SNS alerting on failures. Development VPC isolates SageMaker Studio training jobs, Feature Store integration, and CodePipeline CI/CD that promotes models from dev endpoints through Model Registry to production. QuickSight monitors CloudWatch metrics for model drift detection while VPC peering enables secure cross-environment model artifact transfer. Fork this diagram on Diagrams.so to customize security groups, adjust auto-scaling thresholds, or add your own preprocessing Lambda functions for manufacturing quality control workflows.

People also ask

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

Deploy SageMaker endpoints with auto-scaling behind API Gateway and WAF, use Lambda for image quality checks and orchestration, implement CodePipeline CI/CD with Model Registry for version control, and monitor drift via QuickSight connected to CloudWatch metrics. This diagram shows VPC isolation, Feature Store integration, and secure cross-environment peering.