MLflow
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
MLflow is an open-source platform for the machine learning lifecycle: experiment tracking, reproducible packaging, model deployment, and a model registry.
What is MLflow?
MLflow is an open-source platform for managing the machine learning lifecycle, from experiment tracking through packaging and deployment to a built-in model registry. It originated at Databricks and works with any ML library, so a team keeps one system of record across projects built on different frameworks.
Components of MLflow
- Tracking logs each training run: parameters, metrics, code version, and output files, so any result can be traced back to what produced it.
- Projects packages training code with its dependencies so a run can be reproduced on another machine.
- Models wraps a trained model in a standard format that serving tools can deploy, whatever library built it.
- Model Registry is a built-in model registry: it stores registered models with version numbers and stage labels, and records every promotion between stages.
MLflow vs Weights & Biases
The practical difference: MLflow is open-source software you run yourself and covers the whole lifecycle including a model registry, while Weights & Biases is first a hosted service, strongest at experiment dashboards and team collaboration. Either handles core experiment tracking; the choice tends to follow whether a team wants to operate the tool or subscribe to it.
| Criterion | MLflow | Weights & Biases |
|---|---|---|
| Scope | Full ML lifecycle, including a model registry | Experiment tracking and visualization first |
| Delivery | Open source; self-hosted by default | Hosted platform by default |
| Known for | Running inside your own infrastructure | Dashboards and team collaboration |
Example of MLflow
A team tuning a demand-forecasting model tries sixty combinations of features and hyperparameters. Every run logs its parameters and validation error to MLflow Tracking, so the sixty attempts sit in one sortable table instead of sixty notebook copies.
The best run’s model enters the MLflow registry as version 7 and is promoted to staging. When it passes final checks, the deployment job pulls exactly that artifact – not “the latest file in the folder” – and ships it.
Related AI terms: Wandb · Model Registry · Pipeline · Deploy · Model Training
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