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MLOps Concepts

18 reviews

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

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2 Hours16 Videos47 Exercises
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Course Description

Learn about Machine Learning Operations (MLOps)

Understanding MLOps concepts is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production.

In this course, you’ll learn what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. After learning about the essential MLOps concepts, you’ll be well-equipped in your journey to implement machine learning continuously, reliably, and efficiently.

Discover How Machine Learning Can be Scaled and Automated

How can we scale our machine learning projects using the minimum time and resources? And how can we automate our processes to reduce the need for manual intervention and improve model performance? These are fundamental Machine Learning questions that MLOps provides the answers to.

In this MLOps course, you’ll start by exploring the basics of MLOps, looking at the core features and associated roles. Next, you’ll explore the various phases of the machine learning lifecycle in more detail.

As you progress, you'll also learn about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. You’ll explore key MLOps concepts, giving you a firmer understanding of their applications.
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  1. 1

    Introduction to MLOps


    First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.

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    What is MLOps?
    50 xp
    What is not MLOps?
    50 xp
    WhatOps for what?
    100 xp
    Different phases in MLOps
    50 xp
    The ML lifecycle
    50 xp
    Tasks per phase
    100 xp
    Roles in MLOps
    50 xp
    Your MLOps team
    50 xp
    Core roles in MLOps processes
    100 xp
  2. 3

    Deploying Machine Learning into Production

    In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.

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  3. 4

    Maintaining Machine Learning in Production

    Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.

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In the following tracks

Machine Learning EngineerMLOps Fundamentals


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George Boorman
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James Chapman
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Arne Warnke
Folkert Stijnman HeadshotFolkert Stijnman

ML Engineer

Graduate data scientist with expertise in machine learning and data science. Currently working on a freelance basis for a broad range of companies.
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Don’t just take our word for it

from 18 reviews
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  • Hany N.
    about 2 months

    The course is useful and introduced me to the general concepts of MLOps

  • Sahan D.
    10 months

    Great, very helpful for my corporate work

  • Maykon S.
    about 1 month


  • Olatunji A.
    about 2 months

    Recommend DataCamp to a friend

  • Maja L.
    about 2 months

    Another great course!

"The course is useful and introduced me to the general concepts of MLOps"

Hany N.

"Great, very helpful for my corporate work"

Sahan D.


Maykon S.


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