WEEK 7 — Capstone System Design & Model Building
Theme: Architect & Build a Real AI System
Week 7 is about:
Designing and building the system properly.
Not rushing to deploy.
Week 7 —
6 Lesson Structure
Lesson 1:
Problem Identification & Scope Definition
Focus:
Identifying a real African problem
Narrowing scope properly
Defining measurable objectives
Defining success metrics
Stakeholder thinking
Outcome: Students choose a meaningful, realistic problem.
Lesson 2:
Dataset Strategy & Collection
Focus:
Collecting real datasets
Simulating datasets if necessary
Cleaning & documenting dataset
Ethical data sourcing
Data risk assessment
Outcome: Students prepare production-ready dataset.
Lesson 3:
System Architecture Design
Focus:
Designing full system pipeline
Data flow diagram
Model selection reasoning
Storage decisions
API & integration planning
Deliverable: System architecture diagram.
Outcome: Students think like AI architects.
Lesson 4:
Model Development & Evaluation
Focus:
Building ML/DL model
Feature engineering
Training & tuning
Proper evaluation metrics
Validation strategy
Deliverable: Model evaluation report draft.
Outcome: Students build reliable model core.
Lesson 5:
Integrating GenAI or RAG Component
Focus:
Adding LLM functionality
Designing RAG if applicable
Prompt engineering
Controlled output handling
Guardrails setup
Outcome: Students integrate modern AI capability.
Lesson 6:
Bias Testing & Ethics Review
Focus:
Bias detection
Fairness evaluation
Testing edge cases
Risk assessment
Mitigation strategy
Deliverable: Ethics & bias mitigation explanation.
Outcome: Students build responsible systems.
Week 7 Final Outcome
By the end of Week 7, students have:
✔ Identified a real-world African problem
✔ Designed full system architecture
✔ Built and evaluated a model
✔ Integrated GenAI or RAG (if relevant)
✔ Conducted bias & ethics testing
✔ Produced core technical documentation
Week 7 produces:
A working AI system prototype (pre-deployment).
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