ModelRefs / MLflow — AI Glossary
MLflow — AI Glossary
An open-source ML lifecycle platform for experiment tracking, model packaging, registry, and deployment.
Overview
MLflow (Databricks 2018) provides four components: Tracking (log metrics, params, artifacts per run), Projects (reproducible run packaging), Models (standardized model format with flavors), and Registry (model versioning and stage management). The default experiment tracking solution for many enterprise ML teams; integrated with Databricks and Azure ML.
Reference details
| Topic | infrastructure |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
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Frequently asked questions
What is MLflow?
An open-source ML lifecycle platform for experiment tracking, model packaging, registry, and deployment.
What concepts are related to MLflow?
Closely related concepts include experiment tracking, model registry, weights and biases.