Applied AI
Start with the problem, not the model.
Applied AI is about deciding where AI belongs in real work, what evidence is needed to trust the output and where human judgement remains essential.
- Problem framingDefine the decision, user, constraints and success criteria before selecting a model or tool.
- Data and contextUnderstand what the system knows, what it does not know and how source quality shapes the result.
- EvaluationTest outputs against clear criteria, failure modes and the consequences of being wrong.
- Workflow designPlace AI inside a process with explicit handoffs, review points and accountability.
Built for practical use.
The learning connects AI to cybersecurity, governance and cloud because those boundaries meet in production systems. Learners practise explaining why an AI-enabled approach is appropriate, how it is checked and what remains outside the model’s authority.
View the common curriculum