MLflow Concepts: Understanding Experiments, Runs, and Organization

MLflow’s power comes from how it organizes your machine learning work. Understanding experiments, runs, and their relationships is essential to using MLflow effectively.

The Hierarchy: Experiments → Runs → Metadata/Artifacts

MLflow organizes your work in a clear three-level hierarchy.

At the top are Experiments — organizational containers for related work. Within experiments are Runs — individual executions of your code. Within runs are Metadata and Artifacts — the parameters, metrics, and files produced.

This structure mirrors how data scientists actually work. You explore multiple approaches to a problem, run many variations of each approach, and need to track what each variation produced.

MLflow Experiments: Organizing Related Work

An experiment is a higher-level organizational unit that encompasses a set of runs.

What Experiments Represent

Experiments group and organize related runs, typically conducted to explore different configurations, parameters, or algorithms.

You might create an experiment for:

• “Customer Churn…