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

Quickstart elements

You can read through the quickstart as a guide, or navigate directly to the notebook example to get started.

MLflow Tracking Quickstart Guide

Learn the basics of MLflow Tracking in a fast-paced guide with a focus on seeing your first model in the MLflow UI

MLflow Tracking Quickstart Notebook

See an example of using the MLflow fluent API to log, load, and register a model for inference. Great for code-focused learners!

Logging your first MLflow Model

In this lengthy tutorial, you will walk through the basics of MLflow in a sequential and guided manner. With each subsequent step, you will increase your familiarity with the primary functionality around MLflow Tracking and how to navigate the MLflow UI.

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

Download the Notebook

Guide sections

Interested in navigating directly to the content that you’re curious about? Select the section from each tutorial below!

Setting up the MLflow Tracking Server

Learn how to start an MLflow Tracking Server and the MLflow UI Server locally

Using the MLflow Client

Connect to the Tracking Server with the MLflow Client and learn how to search for experiments

Create your first Experiment

Explore the MLflow UI and create your first MLflow experiment with a unique name and identifying tags

Search Experiments by tags

Learn how to use tags for MLflow experiments and how to leverage them for searching

Creating a Dataset for Testing

Build a synthetic dataset to use while exploring the features of MLflow

Logging your first MLflow run

Train a model using the synthetic dataset and log the trained model, metrics, and parameters

View the full Notebook

See the tutorial notebook in its entirety. If you prefer just reading code, this is the best place to look.

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.

Introduction to autologging

Train a model and use MLflow autologging to automatically record model artifacts, metrics, and parameters

View autologged data in the MLflow UI

See what autologging will autonomously log for you during model training with only a single line of code

Loading a model for inference

Load the autologged model in its native format and use it to generate predictions

15 minute Quickstart - Comparing Runs and Deploying your Best Model

This quickstart tutorial focuses on the MLflow UI’s run comparison feature, provides a brief overview of MLflow Projects, and shows how to register a model. After locally serving the registered model, a brief example of preparing a model for remote deployment via containerizing the model via Docker is covered.

Generate runs

Run an MLflow Project that will perform hyperparameter tuning to generate a large volume of runs

Run comparison

Use the MLflow UI Runs Compare functionality to evaluate the hyperparameter tuning run and select the best model

Register the best model

Learn to register models with the MLflow UI and perform stage transitions from within the UI

Start a local ML inference server

Use the integrated inference server in MLflow to serve your registered model locally

Build a deployable container for your model

Learn how to generate a docker container that houses your model for deployment to external services

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.

5 Minute Tracking Server Overview

Learn how to log MLflow experiments with different tracking servers