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MLOps for Business

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

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3 Hours14 Videos45 Exercises
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Course Description

Discover MLOps for Business

Learn the essential concepts and practices of MLOps, an emerging set of tools and techniques for automating and scaling machine learning applications. Machine learning model development used to be a lengthy manual task, and most models never made it into production. With MLOps, businesses can effectively scale and automate the design, development, and operation of machine learning models.

Learn to Use Machine Learning Operations in Your Business

This course will teach you what MLOps is and how you can use it to become a fully mature machine-learning company. You will learn about the requirements for MLOps, the tools, techniques, and people involved, and how to avoid common pitfalls. You’ll start by learning about the main elements of MLOps and why it is critical for businesses that want to design, develop, and operate multiple machine learning applications.

Explore the MLOps Life Cycle

Next, you’ll explore the entire MLOps life cycle, from design to development, deployment, and operations. In Chapter 3, you’ll learn about the main challenges and risks of deploying machine learning models in practice. Finally, you’ll look at the best practices and case studies for successfully implementing MLOps in the real world. By the end of the course, you'll have a deep understanding of how to design, develop, and operate machine learning applications at scale and will be able to leverage the impact of machine learning on your business.
For Business

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  1. 1

    Introducing MLOps

    Free

    The first chapter will introduce MLOps and why it is necessary for businesses that want to design, develop, and operate multiple machine learning applications simultaneously. You will learn about the main elements of MLOps, such as scaling and automation, its benefits, and why MLOps remain challenging. You will also explore what it takes to start the MLOps journey both from a technological and managerial perspective.

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    What is MLOps?
    50 xp
    MLOps: a set of practices to ...
    50 xp
    What are characteristics of MLOps?
    100 xp
    The business arguments for MLOps
    50 xp
    Relevant MLOps fields
    50 xp
    How to invest in MLOps
    50 xp
    Reasons to invest in MLOps
    100 xp
    Laying the foundation for MLOps
    50 xp
    MLOps team members
    50 xp
    Prerequisites of MLOps
    100 xp
    Elements of MLOps
    100 xp
  2. 3

    MLOps: From Theory to Practice

    In the third chapter, you will move from theory to practice and discover the main challenges and risks of deploying machine learning models. You’ll also learn how MLOps teams successfully operate and what management can do to foster successful scaling machine learning.

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  3. 4

    MLOps in the wild

    The final chapter will demonstrate how to successfully jumpstart your business's MLOps journey by discussing best practices and pitfalls to avoid. Finally, you’ll examine the different levels of MLOps maturity and conclude the course with a real-life case study about designing, developing, and operating a machine learning application for critical production processes.

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For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Collaborators

Collaborator's avatar
Maarten Van den Broeck

Prerequisites

Machine Learning for Business
Arne Warnke HeadshotArne Warnke

Head of Emerging Curriculum at DataCamp

Arne is responsible for machine learning engineering and data engineering content at DataCamp. He is a mathematician with a Ph.D. in economics and applied statistics. Before joining DataCamp, Arne worked for several years as a data scientist.
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