Get started with MLflow experiments

This collection of notebooks demonstrate how you can get up and running with MLflow experiment runs.

MLflow components

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. MLflow has three primary components:

• Tracking

• Models

• Projects

The MLflow Tracking component lets you log and query machine model training sessions (runs) using the following APIs:

• Java

• Python

• R

• REST

An MLflow run is a collection of parameters, metrics, tags, and artifacts associated with a machine learning model training process.

What are experiments in MLflow?

Experiments are the primary unit of organization in MLflow; all MLflow runs belong to an experiment. Each experiment lets you visualize, search, and compare runs, as well as download run artifacts or metadata for analysis in other tools. Experiments are maintained in a Databricks hosted MLflow tracking server.

Experiments are located in the workspace file tree. You manage experiments using the same tools you use to manage other workspace objects such as folders, notebooks, and libraries.

MLflow example notebooks

The following notebooks demonstrate how to create and log to an MLflow run using the MLflow tracking APIs, as well how to use the experiment UI to view the run. These notebooks are available in Python, Scala, and R.

The Python and R notebooks use a notebook experiment. The Scala notebook creates an experiment in the Shared folder.

Note

With Databricks Runtime 10.4 LTS ML and above, Databricks Autologging is enabled by default for Python notebooks.

• Quickstart Python
• Quickstart Java and Scala
• Quickstart R