Course
digicode: TAIGEF
Technical AI Governance Essentials – Foundations
Course facts
- Explaining the holistic approach, principles, and key considerations of AI governance
- Designing architecture patterns for governance-by-design
- Implementing strategies for AI governance across different organizational scales
- Integrating governance techniques within diverse technical environments
- Applying advanced optimization and automation approaches to AI governance
- Developing platform designs for governance-as-a-service
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.
0 The AI governance Imperative: What, how, and why now?
- Explaining the holistic approach, principles, and key considerations of AI governance
1 Setting up infrastructure: Building the Governance Platform
- Designing architecture patterns for governance-by-design
- Discussion: Identifying your technical debt
- Discussion: Technical tools as governance enablers
2 Integrating: Unifying the Governance Platform
- Implementing strategies for AI governance across different organizational scales
- Discussion: Tailoring governance for performance
- Discussion: Measure platform success
- Tabletop exercise: Corrupted data incident
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.
Any technical background – experience implementing production systems, software engineers, solution architects, and security professionals.