Production-grade MLOps architecture separates dev and prod SageMaker endpoints behind Application Load Balancer with WAF protection, both running ml.g5.2xlarge instances with auto-scaling. Training Pipeline feeds SageMaker Training Job which stores artifacts in S3, while Step Functions orchestrates retraining triggered by EventBridge and monitored via SageMaker Model Monitor. Feature Store centralizes feature management across environments, with CloudWatch Monitoring tracking endpoint performance and Lambda functions processing data and orchestrating workflows. This architecture demonstrates AWS best practices for continuous model deployment, automated retraining, and environment isolation critical for regulated ML workloads. Fork this diagram on Diagrams.so to customize instance types, add CI/CD stages, or integrate with your model registry and monitoring stack.