Module 6 Rubric

Artifact

predictive modeling report with baseline comparison, validation evidence, and model card focused on time, drift, and monitoring: Define drift checks and monitoring metrics for a deployed predictor.

Criterion	Excellent	Satisfactory	Needs Revision
Problem framing	Decision, stakeholders, affected population, and constraints are explicit and coherent.	Decision and stakeholders are named, but some constraints are thin.	The work jumps to tools or conclusions without a clear decision frame.
Evidence and method	Uses lab evidence or equivalent analysis correctly; compares a baseline with an alternative; explains limits.	Provides evidence and some comparison, but limits or assumptions are incomplete.	Evidence is asserted without reproducible analysis or baseline comparison.
Domain reasoning	Connects results to Machine Learning & Predictive Modeling with accurate terminology and realistic operational implications.	Uses relevant terminology but misses some operational implications.	Reasoning is generic and could apply to almost any AI course.
Risk and governance	Identifies technical, human, governance, and deployment risks with concrete mitigations.	Identifies major risks but mitigations are vague.	Risks are missing, generic, or treated as afterthoughts.
Communication	Recommendation is concise, defensible, and understandable to analytics lead, domain owner, operations manager, and model risk reviewer.	Recommendation is understandable but not fully defended.	Recommendation is unclear, unsupported, or overclaims what the evidence proves.

Minimum Completion Standard

A passing submission must include a runnable or inspectable evidence artifact, a baseline comparison, at least two failure modes, one mitigation per failure mode, and a specific next-action recommendation.