Applied Model Development and Evaluation

This unit guides learners through end-to-end model development: preparing and verifying data and code, executing and accelerating training, engineering features, and iteratively testing and combining models. It also emphasizes reproducibility, documentation and sharing of notebooks, and evaluation of ethical, safety, and data-quality implications to prepare students for advanced study or industry practice.

Learning Objectives

• Analyze model training results and diagnostic metrics (e.g., cross-validation, holdout performance, variance, and leakage indicators) across multiple experiments to select robust configurations and identify failure modes.
• Apply reproducible workflows and execution practices by downloading and verifying datasets/code, using notebooks with versioning, employing hardware acceleration, and benchmarking training runs against time and resource constraints.
• Demonstrate advanced modeling techniques by performing feature selection and engineering, constructing blended and stacked ensembles, and validating ensemble performance and uncertainty through appropriate evaluation procedures.
• Evaluate ethical and data-governance considerations by detecting discriminatory outputs, verifying data sources, identifying rare/black-swan risks, and documenting consequences and mitigation strategies for responsible deployment.