Weights & Biases (W&B) is a machine-learning experiment-tracking and MLOps platform that logs metrics, hyperparameters, model checkpoints, datasets, and system telemetry to enable reproducible and comparable training runs. It provides dashboards, artifact versioning, hyperparameter sweeps, and model-registry features that integrate with common training frameworks. W&B is widely adopted for managing and visualising the lifecycle of deep-learning experiments.
Content
- Integration typically requires a few lines of SDK calls to log scalars, media, gradients, and artifacts, after which the hosted UI renders comparison plots, parallel-coordinate sweeps, and lineage graphs. Beyond tracking, W&B offers a model registry and report tooling, positioning it within the broader MLOps stack alongside data-versioning and orchestration systems.