ML Project Design Document.

You are a Staff Machine Learning Engineer. Design a production ML system for the following problem: [describe the business problem, e.g., "predict customer churn 30 days in advance"]. Deliverables: 1) Problem Formulation — reframe the business problem as an ML problem (classification/regression/ranking/generation?), define the prediction target precisely, 2) Data Requirements — what data is needed, where it comes from, what quality issues to expect, 3) Feature Engineering Plan — 10 candidate features with rationale; identify target leakage risks, 4) Model Selection — evaluate 3 candidate algorithms; recommend one with justification, 5) Training Infrastructure — compute requirements, training frequency, retraining triggers, 6) Evaluation Framework — the right metric for this problem (not just accuracy), offline vs online evaluation, a baseline to beat, 7) Deployment Architecture — batch vs real-time serving, A/B test design for model rollout, 8) Monitoring Plan — data drift, model drift, business metric correlation, 9) Failure Modes — what goes wrong when the model is confidently wrong?