Kaggle Grandmasters Playbook: 7 Tested Modeling Techniques for Tabular Data (developer.nvidia.com)

Kaggle Grandmasters published a practical “playbook” of seven battle-tested techniques for tabular ML that consistently land top leaderboard finishes — and they stress that the real secret is a repeatable system: fast experimentation + careful validation. The playbook emphasizes robust CV (k‑fold, TimeSeriesSplit, GroupKFold), deep train-vs-test and temporal-target checks to detect distribution shift, and a habit of running diverse baselines (linear, GBDTs, small nets) early to steer model choice. Real-world wins cited include uncovering distribution/temporal issues in the Amazon KDD Cup ’23 and baseline-driven placement in a rainfall forecasting challenge. Where this guide breaks from typical advice is in engineering for scale: every technique is demonstrated with GPU acceleration (cuDF/cuML, GPU XGBoost/LightGBM/CatBoost, CuPy) so teams can generate thousands of features, evaluate thousands of ensemble weight combinations, and fit multi-level stacks in hours instead of days. Key methods covered are large-scale feature engineering (categorical combos, group aggregations), hill-climbing and stacking ensembles (with GPU-vectorized metric evaluation), and multi-round pseudo‑labeling with soft labels and k‑fold safety to avoid leakage. The takeaway for practitioners: combine principled validation with GPU-powered pipelines to explore far more experiments, enabling more reliable, production-ready tabular models on massive datasets.