Enterprise AI Deployment and Operations

This unit focuses on operationalizing AI in the cloud by teaching model deployment, monitoring, platform architecture, and organizational change management. Students will learn to select and prioritize use cases, apply MLOps practices and templates, assess AI maturity, and prepare architectures and processes for scalable enterprise AI initiatives.

Learning Objectives

• Analyze and compare model deployment strategies, core AI services, and ML/ DL platform components to recommend appropriate cloud architectures for enterprise-scale AI
• Apply monitoring strategies and tools to create an ML model monitoring plan that tracks key performance metrics, detects drift, and enforces synchronization across development, staging, and production environments
• Evaluate use cases, PoC versus large-scale initiative trade-offs, and AI maturity assessment results to prioritize projects and define a scalable implementation roadmap with related change management actions
• Demonstrate the ability to produce operational artifacts (org change management plan, MLOps automation guide, and use-case identification sheet) and implement data exploration, preprocessing, and configuration practices required for production-ready models