Getting Started with MLflow

For those new to MLflow or seeking a refresher on its core functionalities, the quickstart tutorials here are the perfect starting point. They will guide you step-by-step through fundamental concepts, focusing purely on a task that will maximize your understanding of how to use MLflow to solve a particular task.

5-minute Quickstart - MLflow Tracking

In this brief introductory quickstart on MLflow Tracking, you will learn how to leverage MLflow to:

  • Log training statistics (loss, accuracy, etc.) and hyperparameters for a model

  • Log (save) a model for later retrieval

  • Register a model to enable deployment

  • Load the model and use it for inference

In the process of learning these key concepts, you will be exposed to the MLflow fluent API, the MLflow Tracking UI, and learn how to add metadata associated with a model training event to an MLflow run.

If you would like to get started immediately by interactively running the notebook, you can:

Download the Notebook

15 minute Quickstart - Autologging in MLflow

In this rapid-pace quickstart, you will be exposed to the autologging feature in MLflow to simplify the logging of models, metrics, and parameters. After training and viewing the logged run data, we’ll load the logged model to perform inference, showing core features of MLflow Tracking in the most time-efficient manner possible.

5 Minute Tracking Server Overview

This quickstart tutorial walks through different types of MLflow Tracking Servers and how to use them to log your MLflow experiments.