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Machine Learning Engineer

Step into the cutting-edge field of machine learning engineering with this comprehensive track designed for aspiring professionals. This program teaches you everything you need to know about model deployment, operations, monitoring, and maintenance. In this track, you will learn the fundamentals of MLOps. You will work interactively with key technologies like Python, Docker, and MLflow. You will learn in detail about concepts such as CI/CD, deployment strategies, or concept drift. The track includes interactive courses and real-world projects that help you facilitate the skills learned. Upon completing this track, you'll emerge as a well-rounded machine learning engineer with all the skills required for a junior machine learning engineer role. Note: Prior knowledge of concepts, including data manipulation, training, and evaluating machine learning models using Python, is expected from learners who enroll in this track.

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Loved by learners at thousands of companies

1
Theory
MLOps Concepts

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

2 hours

Folkert Stijnman Headshot

Folkert Stijnman

ML Engineer

2
Python
End-to-End Machine Learning

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

4 hours

Joshua Stapleton Headshot

Joshua Stapleton

Machine Learning Engineer

3
Theory
Developing Machine Learning Models for Production

Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

4 hours

Sinan Ozdemir Headshot

Sinan Ozdemir

Data Scientist, Entrepreneur, and Author

4
Shell
Introduction to Shell

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

4 hours

DataCamp Content Creator

Course Instructor

5
Python
Predictive Modeling for Agriculture

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

2 hours

6
Theory
MLOps Deployment and Life Cycling

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

4 hours

Nemanja Radojković Headshot

Nemanja Radojković

Senior Data Scientist @ Euroclear

7
Python
Introduction to MLflow

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

4 hours

Weston Bassler Headshot

Weston Bassler

Senior MLOps Engineer

8
Theory
Fully Automated MLOps

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

4 hours

Arturo Opsetmoen Amador Headshot

Arturo Opsetmoen Amador

Senior Consultant - Machine Learning

9
Python
Predicting Temperature in London

Perform a machine learning experiment to find the best model that predicts the temperature in London!

2 hours

Folkert Stijnman Headshot

Folkert Stijnman

ML Engineer

10
Python
Introduction to Data Pipelines

This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.

4 hours

Jake Roach Headshot

Jake Roach

Data Engineer

11
Theory
Machine Learning Monitoring Concepts

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

2 hours

Hakim Elakhrass Headshot

Hakim Elakhrass

Co-founder and CEO of NannyML

12
Python
Monitoring Machine Learning in Python

This course covers everything you need to know to build a basic machine learning monitoring system in Python

3 hours

Hakim Elakhrass Headshot

Hakim Elakhrass

Co-founder and CEO of NannyML

13
Python
Introduction to Docker

Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.

4 hours

Tim Sangster Headshot

Tim Sangster

Full Stack Software Engineer

14
Shell
CI/CD for Machine Learning

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

5 hours

Ravi Bhadauria Headshot

Ravi Bhadauria

Senior Machine Learning Engineer

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Instructors

  • Folkert Stijnman Headshot
    Folkert StijnmanML EngineerSee Folkert Stijnman's Portfolio
  • Joshua Stapleton Headshot
    Joshua StapletonMachine Learning EngineerSee Joshua Stapleton's Portfolio
  • Sinan Ozdemir Headshot
    Sinan OzdemirData Scientist, Entrepreneur, and AuthorSee Sinan Ozdemir's Portfolio
  • DataCamp Content CreatorCourse InstructorSee DataCamp Content Creator's Portfolio

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