Weights & Biases (W&B) is a collaborative MLOps platform that simplifies experiment tracking, model management, and production monitoring for machine learning teams. It centralizes all training runs, hyperparameter sweeps, and model artifacts in a shareable dashboard, enabling teams to compare experiments, discover best-performing models, and reproduce results. Unlike simple logging to files, W&B provides structured tracking, automatic chart generation, and team collaboration features. It integrates with PyTorch, TensorFlow, scikit-learn, XGBoost, and custom training loops.