Operationalizing Enterprise AI

This unit covers the practical steps to deploy, monitor, and scale AI solutions in the cloud, including MLOps practices, environment synchronization, data preprocessing, and model performance tracking. Students will explore AI maturity assessment, organizational change management, use-case selection, and the role of core and specialized AI services (including generative AI) in enterprise and domain-specific platforms.

Learning Objectives

• Analyze organizational AI maturity and prioritized use cases using provided assessment and identification templates to recommend whether to pursue smaller PoC solutions or larger-scale initiatives
• Apply MLOps and deployment best practices to design and implement end-to-end model workflows across development, staging, and production environments, including data exploration, preprocessing, and configuration synchronization
• Demonstrate implementation of model monitoring strategies by tracking key performance metrics, detecting drift/failure modes, and using automation guides to maintain model health and reliability
• Evaluate core and specialized AI services, generative AI integration, and critical enablers to justify architecture and service selections for enterprise and domain-specific (e.g., healthcare) AI/ML platforms