Domain IV · Manage AI Model Development and Evaluation · 16% of the exam
IV.1 Oversee AI/ML model technique(s) (e.g., algorithm, selection)
Task 1 of Manage AI Model Development and Evaluation
May 9: Low Sept 6: Low Study Hall misses: 0 Logged: 0
Enablers (verbatim, ECO)
PMI, ECO p. 5: enablers are "illustrative examples of the work associated with the task" and "not meant to be an exhaustive list but rather offer a few examples to help demonstrate what the task encompasses."
Ours: PMI's stems paraphrase one of these as a symptom; the right option is that enabler in PMI's words; the distractors are its siblings or a neighbouring task's enablers.
- Research and evaluate appropriate algorithms for specific use cases
- Guide selection between supervised, unsupervised, and reinforcement learning approaches
- Assess trade-offs between model complexity, performance, and interpretability
- Coordinate with data scientists on model architecture decisions
- Review algorithm selection criteria and decision documentation
The rule
Guide the choice between supervised, unsupervised and reinforcement approaches from the problem shape, and review the selection criteria. The PM does not pick the algorithm.
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What to read
- Workbook p. 69-76 — Select Cognitive-Relevant Algorithm/Modeling Technique; Ensemble Methods; Usage of AutoML; Fine-Tuning of Pretrained Models; Usage of Generative AI
- Module 04 Lessons 2-6: Introduction to Machine Learning through Other Models; Lesson 8: Automated Machine Learning; Lesson 11: Transfer Learning and Third-Party Models
- Leading and Managing AI Projects p. 28-31 — The Build-Versus-Buy-Versus-Integrate Decision; Training, Fine-Tuning, and Prompt Engineering
- Study guide Lesson 8: ECO Task IV.1 — Oversee AI/ML model technique(s) (e.g., algorithm, selection)
Glossary terms in the enablers: reinforcement learning