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MLOps Concepts

4.5+
29 reviews
Intermediate

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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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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In the following Tracks

Associate AI Engineer for Data Scientists

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Machine Learning Engineer

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Machine Learning in Production in Python

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  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.

    Play Chapter Now
    What is MLOps?
    50 xp
    Explaining MLOps
    50 xp
    MLOps principles
    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. 2

    Design and Development

    Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.

    Play Chapter Now
  3. 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.

    Play Chapter Now
  4. 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.

    Play Chapter Now
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Associate AI Engineer for Data Scientists

Go To Track

Machine Learning Engineer

Go To Track

Machine Learning in Production in Python

Go To Track

In other tracks

MLOps Fundamentals

collaborators

Collaborator's avatar
George Boorman
Collaborator's avatar
James Chapman
Collaborator's avatar
Arne Warnke
Folkert Stijnman HeadshotFolkert Stijnman

ML Engineer

Machine Learning Engineer with 5+ years of expertise in fintech, logistics, and telecom. Specializes in developing scalable ML models, designing end-to-end pipelines, and deploying AI solutions to production, seamlessly aligning with business goals.
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Don’t just take our word for it

*4.5
from 29 reviews
72%
21%
3%
0%
3%
  • Tushar T.
    about 1 month

    Its has good theory with byte sized lectures. Hope they are complement with practical projects or just some toy MLops workflows as home work excercise. Ideas for home work would be appreciated.

  • Arpan G.
    about 1 month

    Good

  • karthik M.
    4 months

    Good theoretical course which lays a strong foundation.

  • Mustafa Ç.
    4 months

    It is good start for mlops. It gives so good idea for starting

  • Dinesh R.
    4 months

    What is most likeable about the course is that it is very concise and still provides very good understanding of the topics.

"Its has good theory with byte sized lectures. Hope they are complement with practical projects or just some toy MLops workflows as home work excercise. Ideas for home work would be appreciated."

Tushar T.

"Good"

Arpan G.

"Good theoretical course which lays a strong foundation."

karthik M.

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