Certified Artificial Intelligence Professional (CAIP) Training — Lesson 2: Data, Features, Models, Evaluation, and Experimentation

Learning Objectives

• Interpret the principles, requirements, roles, and dependencies shaping data, features, models, evaluation, and experimentation.
• Apply the workflow and produce model evaluation notebook that can withstand review.
• Analyze “accuracy hides minority-class failure” and recommend a proportionate evidence-based response.

Detailed Topics

• data acquisition
• data quality
• feature engineering
• model selection
• training
• validation
• evaluation metrics
• experiment tracking
• Competence, communication, governance, documented information, risk, and follow-up

Instructor Demonstration

The instructor demonstrates the data, features, models, evaluation, and experimentation workflow, identifies applicable inputs and responsibilities, records checkpoints in model evaluation notebook, and contrasts the accepted state with a condition where accuracy hides minority-class failure.

Hands-On Exercise

Complete a controlled implementation, audit, analysis, or foundation exercise for Data, Features, Models, Evaluation, and Experimentation. Establish scope and criteria, collect or create evidence, examine “accuracy hides minority-class failure,” reach a supported conclusion, and preserve the result in model evaluation notebook.

1. Confirm objectives, scope, roles, criteria, stakeholders, and expected outputs.
2. Perform planned activities while recording assumptions, evidence, and decisions.
3. Test a normal case, an exception, and a follow-up or improvement condition.
4. Document conclusions, owners, due dates, escalation, and verification requirements.

Scenario Analysis

• Identify the requirement or principle with the strongest influence on data, features, models, evaluation, and experimentation.
• Compare two defensible responses and explain governance, risk, and evidence trade-offs.
• Determine how model evaluation notebook should be approved, retained, reviewed, and updated.

Troubleshooting Guidance

Separate missing evidence from ineffective practice, and distinguish a documentation issue from a control, process, governance, or competence failure. Use model evaluation notebook to trace the earliest divergence. Treat “accuracy hides minority-class failure” as a hypothesis until the response is validated against the relevant requirement and intended outcome.

Validation Checklist

• The scope, criteria, stakeholders, and intended outcome for data, features, models, evaluation, and experimentation are explicit.
• The model evaluation notebook links statements and decisions to objective, reviewable support.
• Responsibilities, competence, communication, risk, legal duties, and change impact were considered.
• Findings, corrective actions, monitoring, review triggers, and improvement ownership are clear.

Lesson Outcome

Learners can apply Data, Features, Models, Evaluation, and Experimentation, explain the reasoning, and defend the result through model evaluation notebook.