ML Training Overview Use this skill for model training across PyTorch, TensorFlow/Keras, JAX/Flax, Hugging Face Transformers/Diffusers/Accelerate/PEFT, scikit-learn, XGBoost, LightGBM, CatBoost, Spark MLlib, and Ray. Optimize for correctness first: validated data, leakage-safe splits, reproducible configuration, meaningful metrics, and a simple baseline before complex distributed or accelerator-heavy runs. Training Readiness Checklist 1. Define task, target, metric, baseline, and acceptance threshold. 2. Validate data schema, label quality, missingness, duplicates, class balance, and train/se…