Weights & Biases

Learning Objectives

• Understand what experiment tracking is and why ML teams need it
• Learn what Weights & Biases records and how Weave extends it to AI applications
• Identify when W&B is the right tool in a machine-learning workflow

What Is Weights & Biases?

Weights & Biases (usually written W&B) solves a problem every machine-learning team hits: model development produces a flood of experiments, each with different settings and results, and without a system that quickly becomes an unreproducible mess. W&B is the tool that brings order to it — automatically logging every training run, its hyperparameters, metrics, datasets, and outputs, so the work is recorded, comparable, and reproducible.

It became the de-facto standard for experiment tracking across research labs and companies. Its newer product, Weave, applies the same discipline to large-language-model applications, adding tracing and evaluation so teams can see what an AI feature is actually doing in production. W&B was acquired by the AI cloud provider CoreWeave in 2025 but continues as a distinct, widely-used product.

Core Capabilities

Experiment Tracking

W&B automatically logs each training run — the configuration, metrics over time, system usage, and results — and presents them in live dashboards. Teams compare runs side by side to see what actually improved a model.

Model and Dataset Versioning

Artifacts in W&B version the models and datasets tied to each experiment, so any result can be traced back to exactly the data and model that produced it — essential for reproducibility and audits.

Weave — LLM Observability and Evaluation

Weave extends W&B to AI applications built on large language models: it traces each call through an app, logs inputs and outputs, and supports systematic evaluation so teams can measure quality and catch regressions before users do.

Reports and Collaboration

W&B turns experiments into shareable reports, so findings move from one engineer's screen to a team decision with the evidence attached.

Strengths

• Industry standard — the most widely adopted experiment-tracking tool, with a mature ecosystem
• Reproducibility by default — automatic logging makes results recordable and comparable without extra effort
• Spans classic ML and LLM apps — Weave brings the same rigor to modern AI features, a fast-growing need
• Strong collaboration — dashboards and reports make model work legible to a whole team

Limitations & Considerations

• Built for practitioners — most valuable to people actually training or building models, not end users
• Another system to adopt — the value comes from instrumenting your code and using it consistently
• Overlaps with alternatives — eval and observability features compete with dedicated tools; teams should pick a coherent stack
• Cost at scale — heavy logging and large teams move you up the paid tiers

Best Use Cases

Getting Started

1. Go to wandb.ai and create a free account
2. Install the library and add a few lines to your training script to start logging
3. Watch runs appear in your dashboard; compare configurations and metrics across experiments
4. For LLM apps, add Weave to trace calls and run evaluations on quality

Key Takeaways

• Weights & Biases is the standard platform for tracking machine-learning experiments
• It logs hyperparameters, metrics, datasets, and results so model work is reproducible and comparable
• Weave extends the same rigor to large-language-model applications with tracing and evaluation
• It is essential infrastructure for anyone training or building AI — less relevant to non-technical users