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MLOps Concepts

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 Exercises3050 XP

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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.
  1. 1

    Introduction to MLOps

    Free

    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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Collaborators

james-datacamp
James Chapman
georgeboorman-759a10b4-a66e-4db4-9ca8-078a6fec6c8a
George Boorman
arnewarnke-95993b70-5646-4477-ad32-81956f03acb7
Arne Warnke

Prerequisites

Understanding Machine LearningUnderstanding Data Engineering
Folkert Stijnman Headshot

Folkert Stijnman

ML Engineer

Graduate data scientist with 2+ years of experience in machine learning and data science. Currently working on a freelance basis for a broad range of companies.
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