Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
---
name: weights-and-biases
description: Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [MLOps, Weights And Biases, WandB, Experiment Tracking, Hyperparameter Tuning, Model Registry, Collaboration, Real-Time Visualization, PyTorch, TensorFlow, HuggingFace]
dependencies: [wandb]
---
# Weights & Biases: ML Experiment Tracking & MLOps
## When to Use This Skill
Use Weights & Biases (W&B) when you need to:
- **Track ML experiments** with automatic metric logging
- **Visualize training** in real-time dashboards
- **Compare runs** across hyperparameters and configurations
- **Optimize hyperparameters** with automated sweeps
- **Manage model registry** with versioning and lineage
- **Collaborate on ML projects** with team workspaces
- **Track artifacts** (datasets, models, code) with lineage
**Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+
… load the full skill through Skill Me