Model Selection, Ethics, and Architecture

This unit guides learners through evaluating foundation models, assessing data and technology needs, and establishing ethical and compliance safeguards for AI initiatives. Building on foundational concepts, students prepare a technical vision, resource plan, and prioritized use case portfolio that set up implementation and deployment in subsequent units.

Learning Objectives

• Analyze organizational and technical gaps by conducting accountability, explainability, bias, and preliminary technology gap analyses and producing a prioritized list of corrective actions and data needs
• Evaluate and justify selection of foundation models and deployment architectures by applying selection criteria, assessing data resource requirements, and comparing trade-offs among fine-tuning, RAG, and agent-based approaches
• Demonstrate compliance and ethical readiness by mapping relevant regulatory requirements, developing governance and mitigation strategies, and creating an explainability and bias-monitoring checklist for chosen use cases
• Design a technical vision and resource plan by defining architecture, estimating budget and personnel allocations, selecting an appropriate technology stack, and delivering an executive-level recommendation for a winning use case