Description

The ‘Technical AI Governance Essentials – Optimization’ course provides a comprehensive roadmap for organizations to mature their AI governance capabilities from a hands-on, technical perspective.

The course’s primary goal is to guide students in establishing, integrating, and orchestrating AI governance platforms, ultimately positioning technical governance as an enabler of speed and innovation. The emphasis is on understanding the holistic technical approach, architecture, and implementation of AI governance in a real-world context.

Learning Objectives

• Explain the holistic approach, principles, and key considerations of AI governance.
• Design architecture patterns for governance-by-design.
• Implement strategies for AI governance across different organizational scales.
• Integrate governance techniques within diverse technical environments.
• Apply advanced optimization and automation approaches to AI governance.
• Develop platform designs for governance-as-a-service.

Prerequisites

Any technical background—experience implementing production systems, software engineers, solution architects, and security professionals.

Who Should Attend

This course is aimed at technical practitioners and technical-adjacent roles, such as data scientists, MLOps engineers, data engineers, cloud security architects, and technical leads responsible for designing, building, and maintaining AI systems. The ideal attendee has significant experience in a technical role and is involved in building AI applications or infrastructure.

Course Outline

Module 0 – The AI governance imperative: What, how, and why now?

Objectives:

• Explain the holistic approach, principles, and key considerations of AI governance.

Module 1 – Orchestrate: Scale governance globally

Objectives:

• Integrate governance techniques within diverse technical environments.

Activities:

• Discussion: Always-on governance
• Quiz questions
• Discussion: The legacy integration challenge

Module 2 – Optimize: Implement AI-powered governance innovation

Objectives:

• Apply advanced optimization and automation approaches to AI governance.
• Develop platform designs for governance-as-a-service.

Activities:

• Discussion: The self-tuning governance platform
• Quiz questions
• Discussion: The future-proof governance architecture
• Tabletop exercise: Uncoordinated optimization incident