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Introduction to MLflow

AdvancedSkill Level

4.7+

Updated 11/2024

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

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MLflowMachine Learning

4 hr

16 videos

51 Exercises

3,750 XP

13,157

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Course Description

Managing the end-to-end lifecycle of a Machine Learning application can be a daunting task for data scientists, engineers, and developers. Machine Learning applications are complex and have a proven track record of being difficult to track, hard to reproduce, and problematic to deploy.

In this course, you will learn what MLflow is and how it attempts to simplify the difficulties of the Machine Learning lifecycle such as tracking, reproducibility, and deployment. After learning MLflow, you will have a better understanding of how to overcome the complexities of building Machine Learning applications and how to navigate different stages of the Machine Learning lifecycle.

Throughout the course, you will deep dive into the four major components that make up the MLflow platform. You will explore how to track models, metrics, and parameters with MLflow Tracking, package reproducible ML code using MLflow Projects, create and deploy models using MLflow Models, and store and version control models using Model Registry.

As you progress through the course, you will also learn best practices of using MLflow for versioning models, how to evaluate models, add customizations to models, and how to build automation into training runs. This course will prepare you for success in managing the lifecycle of your next Machine Learning application.

Prerequisites

Supervised Learning with scikit-learn MLOps Concepts

1

Introduction to MLflow

In this Chapter, you will be introduced to MLflow and how it aims to assist with some difficulties of the Machine Learning lifecycle. You will be introduced to the four main concepts that make up MLflow with a main focus on MLflow Tracking. You will learn to create experiments and runs as well as how to track metrics, parameters, and artifacts. Finally, you will search MLflow programmatically to find experiment runs that fit certain criteria.

What is MLflow?

Components of MLflow

MLflow experiments

MLflow Tracking

Starting a run

Logging a run

How to retrieve active run data?

Querying runs

Search runs query options

Searching runs

2

MLflow Models

In this Chapter, you will be introduced to MLflow Models. The MLflow Models component of MLflow plays an essential role in the Model Evaluation and Model Engineering steps of the Machine Learning lifecycle. You will learn how MLflow Models standardizes the packaging of ML models as well as how to save, log and load them. You will learn how to create custom MLflow Models to provide more flexibility to your use cases as well as how to evaluate model performance. You will utilize the powerful concept of “Flavors” and finally use the MLflow command line tool for model deployment.

Introduction to MLflow Models

Package a machine learning model

Storage Format

What's in an MLmodel file?

Saving and loading a model

Logging and loading a model

Custom models

Creating a custom Python Class

Custom scikit-learn model

Scikit-learn flavor and evaluation

Model serving

Serving a model

Score from a served model

3

Mlflow Model Registry

This Chapter introduces the concept of MLflow called the Model Registry. You will be introduced to how the Model Registry is used to manage the lifecycle of ML models. You will learn how to create and search for models in the Model Registry. You then learn how to register models to the Model Registry and learn how to transition models between predefined stages. Finally, you will also learn how to deploy models from the Model Registry.

Introduction to MLflow Model Registry

Create a Model

Searching Models

Registering Models

Registering existing models

Registering new models

Model stages

Model stages in Model Registry

Transitioning model stages

Model deployment

Serving models from the Model Registry

Loading models from the Model Registry

4

MLflow Projects

In this chapter, you'll gain valuable knowledge on how to streamline your data science code for reusability and reproducibility using MLflow Projects. The chapter begins by introducing the concept of MLflow Projects and walking you through creating an MLproject file. From there, you'll learn how to run MLflow Projects through both the command-line and the MLflow Projects module while also discovering the power of using parameters for added flexibility in your code. Finally, you will learn how to manage steps of the machine learning lifecycle by creating a multi-step workflow using MLflow Projects.

Introduction to MLflow Projects

MLproject file layout

Creating an MLproject

Running MLflow Projects

MLflow run command

MLflow projects module

Specifying parameters

Adding parameters to MLproject

Adding parameters to project run

Creating an MLproject for the ML Lifecycle: Model Engineering

Creating an MLproject for the ML Lifecycle: Model Evaluation

Creating a multi-step workflow: Model Engineering

Creating a multi-step workflow: Model Evaluation

Introduction to MLflow

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FAQs

What experience level is expected for this MLflow course?

This is an advanced course requiring familiarity with pandas, scikit-learn, statistics, MLOps concepts, data engineering, and supervised learning before enrolling.

What are the four main MLflow components covered?

You will learn MLflow Tracking for logging experiments, MLflow Models for packaging and evaluating models, Model Registry for lifecycle management, and MLflow Projects for reproducibility.

Will I learn how to deploy models using MLflow?

Yes. You will use the MLflow command line tool for model deployment and learn how to deploy models directly from the Model Registry after transitioning them through predefined stages.

Can I create custom MLflow models for specialized use cases?

Yes. The Models chapter teaches you to create custom MLflow Models and use the Flavors concept to provide flexibility beyond standard model types supported by MLflow.

What is a multi-step workflow in MLflow Projects?

You will learn to chain multiple machine learning lifecycle steps into a single reproducible workflow using MLflow Projects, with parameters for added flexibility across runs.

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