Weights & Biases

Weights & Biases (commonly called W&B or wandb) is the AI developer platform for tracking machine learning experiments, comparing model runs, and managing the ML lifecycle from experimentation to production.

With two lines of code (wandb.init() and wandb.log()), every training run is automatically captured — metrics, hyperparameters, model architecture, system stats, and media outputs like images, audio, and confusion matrices. All data syncs in real time to a centralised dashboard accessible to the entire team.

W&B's core strength over MLflow is its visualisation layer: parallel coordinates plots for hyperparameter sweeps, embedding projectors for high-dimensional data, and rich media panels for inspecting model outputs. The Sweeps feature automates hyperparameter optimisation using Bayesian search or grid/random strategies. The Registry provides a model versioning and governance layer for promoting models from experiment to production.

W&B is cloud-hosted by default (no infrastructure to manage), with a generous free tier for individuals and an enterprise self-hosted option (W&B Server) for teams with data residency requirements.