Tutorial Overview

In this entry point tutorial to MLflow, we’ll be covering the essential basics of core MLflow functionality associated with tracking training event data.

We’ll start by learning how to start a local MLflow Tracking server, how to access and view the MLflow UI, and move on to our first interactions with the Tracking server through the use of the MLflow Client.

The tutorial content builds upon itself, culminating in successfully logging your first MLflow model.

The topics in this tutorial cover:

• Starting an MLflow Tracking Server (Optionally) and connecting to a Tracking Server

• Exploring the MlflowClient API (briefly)

• Understanding the Default Experiment

• Searching for Experiments with the MLflow client API

• Understanding the uses of tags and how to leverage them for model organization

• Creating an Experiment that will contain our run (and our model)

• Learning how to log metrics, parameters, and a model artifact to a run

• Viewing our Experiment and our first run within the MLflow UI

To get started with the tutorial, click NEXT below or navigate to the section that you’re interested in:

• Starting the MLflow Tracking Server
• Using the MLflow Client API
• Creating Experiments
• Searching Experiments
• Create a dataset about apples
• Logging our first runs with MLflow
• Logging Your First MLflow Model Notebook

If you would instead like to download a notebook-based version of this guide and follow along locally, you can download the notebook from the link below.

Download the Notebook