Weights & Biases (W&B)

Weights & Biases (W&B) provides MLOps infrastructure for tracking experiments, versioning datasets and models, evaluating LLM applications, and orchestrating distributed training runs.

When to Use

• Enterprise ML Experiment Tracking: Tracking hyperparameters, training loss curves, GPU utilization, and system metrics.
• Hyperparameter Tuning with W&B Sweeps: Automated Bayesian, grid, and random sweeps across distributed compute nodes.
• Model & Dataset Versioning (Artifacts): Storing, versioning, and tracking data lineage for datasets and model checkpoints.
• Production Model Evaluation & LLM Tracing: Evaluating LLM prompts and agentic tool calls with Weave / W&B Prompts.

Quick Start

import wandb

# Initialize experiment run
wandb.init(project="transformer-pretraining", config={"lr": 0.001, "batch_size": 32})