Skip to main content
HomeMachine Learning

Course

MLOps Deployment and Life Cycling

Advanced
4.8+
132 reviews
Updated 05/2025
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
Start Course for Free

Included withPremium or Teams

TheoryMachine Learning4 hours16 videos54 Exercises3,650 XP7,064Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

Course Description

MLOps Deployment and LifeCycling

Explore the modern MLOps framework, including the lifecycle and deployment of machine learning models. In this course, you’ll learn to write ML code that minimizes technical debt, discover the tools you’ll need to deploy and monitor your models, and examine the different types of environments and analytics you’ll encounter.

Learn About the MLOps Lifecycle

After you’ve collected, prepared, and labeled your data, run numerous experiments on different models, and proven your concept with a champion model, it’s time for the next steps. Build. Deploy. Monitor. Maintain. That is the life cycle of your model once it's destined for production. That is the Ops part of MLOps. This course will show you how to navigate the second chapter of your model's journey to value delivery, setting the benchmark for many more to come. You’ll start by exploring the MLOps lifecycle, discovering the importance of MLOps and the key functional components for model development, deployment, monitoring, and maintenance.

Develop ML Code for Deployment

Next, you’ll learn how to develop models for deployment and how to write effective ML code, leverage tools, and train ML pipelines. As you progress, you’ll cover how to deploy your models, exploring different deployment environments and when to use them. You’ll also develop strategies for replacing existing production models and examine APIs.

Learn How to Monitor Your Models

As you complete the course, you’ll discover the crucial performance metrics behind monitoring and maintaining your ML models. You’ll learn about drift monitoring in production, as well as model feedback, updates, and governance. By the time you’re finished, you’ll understand how you can use MLOps lifecycle to deploy your own models in production.

Prerequisites

MLOps Concepts
1

MLOps in a Nutshell

Start Chapter
2

Develop for Deployment

Start Chapter
3

Deploy and Run

Start Chapter
4

Monitor and Maintain

Start Chapter
MLOps Deployment and Life Cycling
Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Enroll now

Don’t just take our word for it

*4.8
from 132 reviews
86%
14%
0%
0%
0%
  • Aziz
    about 10 hours

    perfect

  • Tomasz
    about 18 hours

  • Juan
    1 day

  • Miracle
    3 days

  • Sebastian
    5 days

  • Štěpánka
    6 days

"perfect"

Aziz

Tomasz

Juan

FAQs

Join over 16 million learners and start MLOps Deployment and Life Cycling today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.