As organizations integrate artificial intelligence into enterprise systems, the role of the AI professional is expanding from building isolated models to managing large-scale, production-grade ecosystems. In this course, learners will explore how to design, deploy, and govern AI infrastructure that is scalable, reliable, and ethical - bridging the gap between data science, engineering, and operations. Through a series of applied labs, they'll work hands-on with cloud environments, containerization, and data platforms to operationalize machine learning pipelines and maintain high-performing AI systems. Across five competencies, learners will architect end-to-end pipelines, manage data infrastructure, and implement automated workflows that support continuous integration and model retraining. They'll evaluate governance, security, and optimization frameworks while developing strategic thinking for scaling AI infrastructure responsibly within complex organizations. The course culminates in a capstone project where learners synthesize their technical and managerial skills to produce a comprehensive AI Infrastructure Strategy - demonstrating mastery in building, securing, and leading enterprise-grade AI systems.