Skip to main content
HomeMachine Learning

Course

Developing Machine Learning Models for Production

IntermediateSkill Level
4.8+
411 reviews
Updated 11/2024
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Start Course for Free
TheoryMachine Learning4 hr13 videos44 Exercises2,850 XP8,337Statement 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.

Loved by learners at thousands of companies

Group

Training 2 or more people?

Try DataCamp for Business

Course Description

Much of today’s machine learning-related content focuses on model training and parameter tuning, but 90% of experimental models never make it to production, mainly because they were not built to last. In this course, you will see how shifting your mindset from a machine learning engineering mindset to an MLOps (Machine Learning Operations) mindset will allow you to train, document, maintain, and scale your models to their fullest potential.

Experiment and Document with Ease

Experimenting with ML models is often enjoyable but can be time-consuming. Here, you will learn how to design reproducible experiments to expedite this process while writing documentation for yourself and your teammates, making future work on the pipeline a breeze.

Build MLOps Models For Production

You will learn best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible.

Scale Up and Automate your ML Pipelines

By considering model and data complexity and continuous automation, you can ensure that your models will be scaled for production use and can be monitored and deployed in the blink of an eye.

Once you complete this course, you will be able to design and develop machine learning models that are ready for production and continuously improve them over time.

Prerequisites

MLOps ConceptsSupervised Learning with scikit-learn
1

Moving from Research to Production

This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.
Start Chapter
2

Ensuring Reproducibility

In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.
Start Chapter
3

ML in Production Environments

In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.
Start Chapter
4

Testing ML Pipelines

In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.
Start Chapter
Developing Machine Learning Models for Production
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 reviewEnroll Now

Don’t just take our word for it

*4.8
from 411 reviews
82%
18%
1%
0%
0%
  • Hamoud
    3 days ago

  • willy
    4 days ago

  • Nina
    4 days ago

  • Jean
    last week

  • Nicholas
    last week

  • Paulo Rogerio
    last week

willy

Nina

Jean

FAQs

How does this course differ from a typical machine learning training course?

Instead of focusing on model training and tuning, this course teaches the MLOps mindset needed to move models into production, covering reproducibility, deployment, monitoring, and testing.

What does the course mean by reproducibility in machine learning?

Chapter 2 covers techniques and best practices for ensuring your models produce consistent results over time, including version control, environment management, and documentation strategies.

What deployment approaches for ML models are discussed?

Chapter 3 examines various strategies for deploying ML models in production environments, along with monitoring and maintenance practices to keep models performing reliably.

Is hands-on coding involved or is this mostly theory?

This is primarily a theory course with 45 exercises. While it lists Python and pandas prerequisites, the focus is on concepts and best practices for production ML rather than coding projects.

What testing methods for ML pipelines will I learn?

Chapter 4 covers various techniques for testing and validating ML pipelines to ensure they perform as expected, including strategies for catching issues before they reach production.

    Join over 19 million learners and start Developing Machine Learning Models for Production 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.

    Grow your data skills with DataCamp for Mobile

    Make progress on the go with our mobile courses and daily 5-minute coding challenges.