Weights & Biases
By Weights & Biases
Weights & Biases (W&B) is an MLOps platform for tracking machine learning experiments, visualizing training metrics, managing model versions, and collaborating on model development.
Definition
Weights & Biases (W&B) is an MLOps platform for tracking machine learning experiments, visualizing training metrics, managing model versions, and collaborating on model development.
Overview
Weights & Biases lets machine learning teams log metrics, hyperparameters, and artifacts from every training run, then compare results across experiments in interactive dashboards to understand what changes actually improve model performance. This is critical in machine learning and deep learning workflows, where dozens or hundreds of training runs with different configurations are common before a final model is selected. Beyond experiment tracking, W&B offers a model registry for versioning and managing trained models through their lifecycle, dataset and artifact versioning, and hyperparameter optimization (sweeps) that automatically search for the best-performing configuration. It integrates with popular frameworks like PyTorch and TensorFlow, as well as Hugging Face's training libraries. W&B is widely used by both research teams and production ML engineering teams, and competes with tools like Comet ML and MLflow in the experiment-tracking and MLOps space, often taught alongside courses like MLOps & Model Deployment.
Key Features
- Experiment tracking for metrics, hyperparameters, and artifacts
- Interactive dashboards for comparing training runs
- Model registry for versioning and lifecycle management
- Automated hyperparameter optimization (sweeps)
- Dataset and artifact versioning
- Integrations with PyTorch, TensorFlow, and Hugging Face
- Team collaboration and reporting tools for ML projects