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DataCamp

MLOps for Business

via DataCamp

Overview

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


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.

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.

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.

Syllabus

  • Introducing MLOps
    • 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.
  • The MLOps Life Cycle
    • In the second chapter, you’ll learn about the entire MLOps life cycle from design to development, deployment, and operations. You’ll explore why monitoring is essential for productive machine learning applications and why we must regularly re-train machine learning models.
  • 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.
  • 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.

Taught by

Arne Warnke

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