Weights & Biases

Weights and Biases (W&B) is a leading MLOps platform that provides machine learning and AI teams with tools for experiment tracking, model visualization, hyperparameter optimization, dataset management, model registry, and model monitoring. By centralizing the ML development workflow in a single platform, W&B enables teams to move from experimentation to production more efficiently and with greater reproducibility.

The W&B Runs dashboard is where most practitioners begin their interaction with the platform. Each training run is automatically logged as a Weights and Biases Run, capturing metrics such as loss, accuracy, and custom evaluation scores across training steps, along with system metrics such as GPU utilization, memory usage, and training speed. Runs are organized in Projects, allowing all experiments for a given model or task to be viewed, compared, and analyzed together.

Experiment tracking in W&B is activated by adding just a few lines of code to any training script. The wandb.init() call begins a run, wandb.log() records metrics and artifacts, and wandb.finish() closes the run. The SDK is compatible with PyTorch, TensorFlow, Keras, Hugging Face, JAX, XGBoost, scikit-learn, and virtually any other Python-based ML framework. Automatic integrations for popular frameworks capture metrics without any manual logging code.

W&B Sweeps is the hyperparameter optimization system. Teams define a search space for hyperparameters such as learning rate, batch size, and architecture choices, and W&B Sweeps automatically runs experiments across the search space using strategies such as grid search, random search, and Bayesian optimization. Results are visualized in parallel coordinates plots and scatter plots that make it easy to identify the most important hyperparameters and their optimal values.

Artifacts in W&B provide versioned data and model storage. Datasets, model weights, evaluation results, and other files can be stored as Artifacts with version history, lineage tracking, and metadata. The lineage view shows the complete chain from raw data through preprocessing steps, training runs, and model artifacts, providing full provenance for any trained model.

W&B Model Registry is the centralized hub for managing model lifecycle from development through production. Models trained with W&B can be registered, versioned, tagged with lifecycle stages (staging, production, archived), and linked to the training runs and datasets that produced them. Integration with deployment systems enables automated promotion of newly registered models to inference environments.

W&B Weave is an LLM application evaluation and monitoring platform for teams building on top of large language models. It provides tracing, annotation, evaluation, and experiment tracking capabilities specifically designed for the unique challenges of LLM-powered applications.

W&B is used by thousands of teams at organizations including leading AI research labs, autonomous vehicle companies, drug discovery firms, and technology companies building AI products.