In this post, we focus on implementing this architecture with step-by-step guidance and reference code. We provide a detailed technical walkthrough that addresses the needs of two critical personas in the AI development lifecycle: the administrator who establishes governance and infrastructure through automated templates, and the data scientist who uses SageMaker Unified Studio for model development without managing the underlying infrastructure.