IV.3 Manage AI/ML model training

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.

1. Plan training schedules and resource allocation for model development
2. Monitor training progress and computational resource utilization
3. Coordinate hyperparameter tuning and optimization activities
4. Oversee cross-validation and model selection processes
5. Manage training data versioning and experiment tracking

Study Hall misses on this task (2)

• Full-length Q101 · confidence High · 38s
  Scenario: Repeated training failures from insufficient compute
  Decided by: Assess requirements and provision infrastructure before training
  Chose: More stakeholder reviews (PM-sounding, does not fix the cause)
• Full-length Q57 · confidence High · 66s
  Scenario: Initial training done; NEXT (Phase IV sequence)
  Decided by: Tune hyperparameters on the validation set; unseen-data testing comes after
  Chose: Jumping to generalisation testing (two steps ahead)

Nothing logged yet.

What to read

• Workbook p. 78-80 — Model Training/Model Building; Hyperparameter Optimization
• Module 04 Lesson 7: What Is AI Model Development?; Lesson 13: Introduction to Model Validation; Lesson 14: Generalizing to New Data
• Module 03 Lesson 24: Splitting Data Sets
• Study guide Lesson 14: ECO Task IV.3 — Manage AI/ML Model Training