This summary is based on the Managing and Evaluating AI Projects course from Atingi.org, completed under the program AI for Sustainable Development Goals.
The course bridges the gap between technical AI knowledge and project management practices, helping professionals (QA, PMs, engineers, and analysts) effectively manage and evaluate AI projects for real-world impact.
Core Idea: A model is only as good as the way it’s evaluated.
- Introduced evaluation metrics for different ML problem types:
- Classification → Accuracy, Precision, Recall, F1, AUC-ROC
- Regression → MAE, RMSE, R² Score
- Discussed trade-offs (e.g., high precision vs. high recall in fraud detection).
- Highlighted business context alignment → metrics should map to the actual value delivered, not just technical performance.
- Addressed overfitting/underfitting as common pitfalls in evaluation.
Core Idea: AI projects succeed only when the right team is in place.
- Roles covered:
- Data Scientists → building and experimenting with models.
- ML Engineers → scaling, deployment, and optimization.
- Product Managers → ensuring alignment with business goals.
- Domain Experts → providing contextual knowledge.
- QA & Testers → validating fairness, performance, and usability.
- Importance of cross-functional collaboration and shared accountability.
Core Idea: AI is not just a tool, it’s an organizational capability.
- Explored structures:
- Centralized AI team (specialist hub).
- Decentralized AI team (embedded in business units).
- Hybrid structures (balance between flexibility and specialization).
- Emphasized AI governance and ethics → bias reduction, transparency, accountability.
- Showed how AI readiness includes strategy, infrastructure, and culture—not just hiring a few ML engineers.
Core Idea: AI products must be usable, fair, and trustworthy.
- Practices covered:
- Human-centered design → solutions must solve real needs.
- Prototyping and iteration → start small, refine quickly.
- Explainability → ensure models are interpretable.
- Fairness & inclusivity → minimize bias and unintended harm.
- Integration → design AI that fits naturally into workflows.
Core Idea: Strategic planning tool for AI projects.
- AI Canvas components explained:
- Prediction task → what the AI needs to predict.
- Training data → quality, availability, and relevance.
- Evaluation → defining success metrics.
- Impact → business value, user impact, ethical considerations.
- Practical exercises on applying AI Canvas to real project scenarios.
Earned the certificate:
Managing and Evaluating AI Projects – AI for Sustainable Development Goals ✅
- Evaluation is not just math → it’s about aligning metrics with value.
- Successful AI projects require cross-functional teams, including QA.
- AI adoption is a matter of organization and governance, not just tech.
- Best practices in design ensure trustworthy, human-centered AI solutions.
- The AI Canvas is a practical, easy-to-use tool for AI project planning.
- Course Platform: Atingi.org