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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
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
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
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

Filip Schouwenaars Headshot

Filip Schouwenaars

Data Science Instructor at DataCamp

4
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

5
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

6
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

7
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

8
ETL and ELT in Python

Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.

4 hours

Jake Roach Headshot

Jake Roach

Data Engineer

9
Introduction to Data Versioning with DVC

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

3 hours

Ravi Bhadauria Headshot

Ravi Bhadauria

Senior Machine Learning Engineer

10
Monitoring Machine Learning 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

11
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

12
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

13
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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Prerequisites

  • Data Scientist

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