Phases of ML Modeling

ML systems should evolve through four distinct phases rather than jumping straight to complex models. Start with simple heuristics and rules (Phase 1), then move to basic ML models like logistic regression (Phase 2), optimize through feature engineering and hyperparameter tuning (Phase 3), and only adopt complex models like deep neural networks when simpler approaches are exhausted (Phase 4). This staged approach reduces risk, improves debuggability, and ensures each phase's best model becomes the baseline for the next, encouraging incremental progress and evidence-driven decision-making.