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Azure ML and AWS SageMaker take fundamentally different approaches to model training infrastructure. Azure uses persistent, workspace-centric compute resources managed centrally and shared across teams, with environments treated as reusable assets. SageMaker provisions compute on-demand per job, requiring developers to explicitly configure instances each time. For environments, Azure provides curated framework environments and custom Docker support, while SageMaker offers three customization levels: built-in algorithms, bring-your-own-script with prebuilt containers, and fully custom Docker images. Azure's modular architecture is more beginner-friendly, while SageMaker's job-centric design offers greater control and scalability for mature MLOps teams.

AWS vs. Azure: A Deep Dive into Model Training – Part 2