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Daily Dose of Data Science | Avi Chawla | Substack

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.

Phases of ML Modeling

Phase 1: before ML (heuristics and rules)

P.S. For those wanting to develop “Industry ML” expertise:

<p>Great Information! This staged approach is basically MLOps maturity in one post. The best part: each phase becomes the benchmark for the next, so you don’t lose track of what “better” actually means.</p>
