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Weights & Biases
The ML experiment tracking and LLM observability platform used by top AI teams.
0Free-Limited
MLOps AI Observability
Overview
Weights & Biases (W&B) is the MLOps platform for experiment tracking, dataset versioning, model evaluation, and LLM monitoring — used by teams at OpenAI, NVIDIA, and Samsung.
Key Features
- Automatic experiment tracking with one line of code
- Sweeps for automated hyperparameter optimization
- Artifacts for dataset and model versioning
- W&B Tables for visualizing model predictions
- Weave for LLM tracing, evaluation, and monitoring
- Reports for shareable ML research documentation
Real-World Workflows
Hyperparameter sweep across GPU cluster
- 1Define a sweep config with parameter search space
- 2Initialize sweep: sweep_id = wandb.sweep(sweep_config)
- 3Launch agents on each GPU: wandb agent sweep_id
- 4View live parallel run comparison in W&B dashboard
Getting Started
pip install wandb
wandb login
import wandb
wandb.init(project='my-project')
wandb.log({'loss': 0.4, 'accuracy': 0.92})
wandb.finish()Compare Alternatives
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