How would you design a scalable Azure ML architecture for enterprise workloads?

A scalable Azure ML architecture uses modular workspaces, autoscaling compute, MLOps pipelines, centralized governance, distributed training, and managed deployment endpoints.

Enterprise-grade Azure ML architectures must support scalability, reliability, security, governance, and operational efficiency. A well-designed architecture separates environments such as development, staging, and production while integrating CI/CD pipelines and centralized monitoring.

Core architectural components include: