McGill ML Showcases Documentation

Welcome to the documentation site for mcgill-showcases.

This repo is organized as learning-by-doing showcase projects with reproducible scripts, clear artifacts, and track-based progression paths.

Start Here

1. Run local setup from README.md or Getting Started.
2. Pick a track based on your goal.
3. Run one showcase end-to-end.
4. Interpret generated artifacts with Coverage Matrix.

Learning Tracks

• Foundations: core supervised, unsupervised, EDA, and feature engineering workflows.
• Production: serving, drift monitoring, rollout decisions, and system patterns.
• Ranking: grouped ranking modeling and API productization.
• Forecasting: time-aware demand modeling and observability-ready APIs.
• Responsible AI: fairness, explainability, and causal decision support.
• Optimization: HPO and policy optimization workflows.

Project Deep Dives

• Deep dive overview
• Supervised learning deep dive
• Causal inference deep dive
• MLOps drift deep dive
• Ranking track deep dive
• Forecasting track deep dive

Contributor Entry

• Showcase architecture map
• New project checklist
• Troubleshooting and FAQ

API Reference

• API overview
• Ranking API docs
• Demand API docs