Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1

Microsoft Azure ML and AWS SageMaker are leading platforms for managing the machine learning lifecycle, enabling scalable model training through jobs. Azure ML employs a workspace-centric approach with user-level, role-based access control (RBAC), which is ideal for managing which users can access specific resources. In contrast, AWS SageMaker uses a job-level permission model via IAM roles, decoupling individual user permissions from the job's execution requirements for better automation and MLOps practices. This comparison highlights how Azure's structure is intuitive for user access control, while AWS's design is suited for large teams with granular pipeline definitions and isolated environments.