Machine Learning Problem Solving Framework Questions

Present a structured end to end approach to machine learning problems: clarify the business goal and constraints, define success metrics, audit and prepare data, design candidate features and baselines, select models and evaluation protocols, iterate on error analysis, and plan deployment and monitoring. Include considerations for trade offs among accuracy, latency, and scalability, and produce a prioritized plan with milestones, experiments, and rollback criteria.