---

name: experiment-tracking description: Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics" version: 1.0.0

Experiment Tracking

Track ML experiments, metrics, and models.

Comparison

---

MLflow

Open-source platform from Databricks.

Core components:

• Tracking: Log parameters, metrics, artifacts
• Projects: Reproducible runs (MLproject file)
• Models: Package and deploy models
• Registry: Model versioning and staging

Strengths: Self-hosted, open-source, model registry, framework integrations Limitations: Basic visualization, less collaborative features

Key concept: Autologging for major frameworks - automatic metric capture with one line.

---

Weights & Biases (W&B)

Cloud-first experiment tracking with excellent visualization.

Core features:

• Experiment tracking: Metrics, hyperparameters, system stats
• Sweeps: Hyperparameter search (grid, random, Bayesian)
• Artifacts: Dataset and model versioning
• Reports: Shareable documentation

Strengths: Beautiful visualizations, team collaboration, hyperparameter sweeps Limitations: Cloud-dependent, limited self-hosting

Key concept: wandb.init() + wandb.log() - simple API, powerful features.

---

What to Track

---

Model Registry Concepts

---

Decision Guide

Resources

• MLflow: https://mlflow.org/docs/latest/
• W&B: https://docs.wandb.ai/