Ai Ml Data Science

A comprehensive reference for taking data from exploration through production models. Covers the full lifecycle: EDA with drift detection, feature engineering with train-serve parity checks, model selection starting with strong tabular baselines (LightGBM, CatBoost), evaluation with slice analysis, and SQLMesh for transformation layers. The modern emphasis is smart: feature stores, automated retraining triggers, and continuous monitoring with Evidently. Cross-references related skills like MLOps and time series, which is helpful when your project spans multiple domains. The decision tree and leakage prevention guidance are especially practical. Best for teams building end-to-end ML systems who need reproducible pipelines and production-ready artifacts, not just notebooks.