What does experiment tracking solve, and how do MLflow and Weights and Biases differ in practice?
Experiment tracking captures the full reproducibility context of a training run — code version, hyperparameters, dataset hash, environment, and metrics — so any result can be reproduced and compared. MLflow is an open-source, self-hosted lifecycle platform; Weights and Biases is a hosted, collaboration-first product with richer real-time visualisation.
How to think about it
Without experiment tracking, a team of three data scientists running 50 experiments each produces a spreadsheet archaeology problem. Tracking turns experiments into a queryable, reproducible database.
What experiment tracking captures
A complete experiment record includes: the Git commit hash of the training code, hyperparameters, the dataset version or hash, the environment (Python version, library versions, CUDA version), per-step training metrics (loss, accuracy), evaluation metrics on validation and test sets, artifact pointers (saved model files, confusion matrices, example predictions), and run duration and hardware.
With this, you can reproduce any result, compare runs on a common metric axis, and explain to a stakeholder why a particular model was chosen.
MLflow
MLflow is an Apache-licensed open-source framework deployed self-hosted (or on Databricks). It has four components: Tracking (experiment logging via a Python SDK), Projects (packaging training code for reproducible runs), Models (a model format with a standard inference interface), and Registry (versioned model lifecycle management with staging and production tags).
MLflow fits teams that need on-premise data residency, tight integration with Databricks or Spark pipelines, and a model registry that feeds directly into a serving layer. The UI is functional but sparse; the API is stable and language-agnostic.
Weights and Biases (W&B)
W&B is a hosted SaaS platform (with a private cloud option) that emphasises collaboration and real-time visibility. Its key differentiators are: live streaming of metrics during training with interactive dashboards; rich media logging (images, audio, point clouds, video) with direct comparison across runs; Sweeps for Bayesian hyperparameter optimisation; Artifacts for dataset and model versioning with lineage graphs; and Reports for sharing experiment summaries with stakeholders.
W&B is preferred for research-heavy teams and LLM fine-tuning workflows where visualising attention maps, generation samples, and sweep results interactively matters. Data residency requirements or air-gapped environments are its primary limitation.
Choosing between them
| Concern | MLflow | W&B |
|---|---|---|
| On-premise / air-gapped | Yes | Limited |
| Model registry + serving | First-class | Via integrations |
| Real-time dashboards | Basic | Rich |
| Hyperparameter sweeps | Via plugins | Native (Sweeps) |
| Cost | Self-hosted infra | Per-seat SaaS |