AI and Machine Learning: post #1709 — TG.ME

Most Data Scientists structure their projects wrong. Use this clean, production-ready layout👇 1️⃣ config/ – config files Separate params from code (local.yaml, prod.yaml) 2️⃣ data/ – full data lifecycle raw → preprocessed → features → predictions 3️⃣ entrypoint/ – main scripts train.py (pipeline) inference.py (batch/real-time) 4️⃣ notebooks/ – exploration only EDA, analysis — never production logic 5️⃣ src/ – core ML code feature engineering, training, inference (modular + testable) 6️⃣ tests/ – automated checks prevent silent failures 7️⃣ docker + env files – reproducibility same setup on any machine/CI 8️⃣ pinned dependencies – stability exact versions → consistent results

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