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Introduction to MLflow

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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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.

  1. 1

    Introduction to MLflow

    Free

    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.

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    What is MLflow?
    50 xp
    Components of MLflow
    50 xp
    MLflow experiments
    100 xp
    MLflow Tracking
    50 xp
    Starting a run
    100 xp
    Logging a run
    100 xp
    How to retrieve active run data?
    50 xp
    Querying runs
    50 xp
    Search runs query options
    50 xp
    Searching runs
    100 xp
  2. 2

    MLflow Models

    Free

    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.

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  3. 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.

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  4. 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.

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In the following tracks

Machine Learning Engineer

Collaborators

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George Boorman
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Arne Warnke
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Kat Zahradova
Weston Bassler HeadshotWeston Bassler

Senior MLOps Engineer

Former DevOps turned MLOps. I currently work at Emburse where I enjoy building and scaling ML projects. I have a passion for bridging the gap between Machine Learning and Operations, automating ML workflows and pushing technology boundaries.
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