Udacity Logo
Log InJoin for Free

Machine Learning DevOps Engineer

Nanodegree Program

The Machine Learning DevOps Engineer Nanodegree program focuses on the software engineering fundamentals required to successfully streamline the deployment of data and machine-learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.

The Machine Learning DevOps Engineer Nanodegree program focuses on the software engineering fundamentals required to successfully streamline the deployment of data and machine-learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.

Advanced

4 months

Real-world Projects

Completion Certificate

Last Updated December 5, 2023

Skills you'll learn:
Git • Machine learning ops troubleshooting • Dvc • Automated machine learning
Prerequisites:
Command line interface basics • Basic descriptive statistics • Jupyter notebooks

Courses In This Program

Course 1 45 minutes

Welcome to the Machine Learning DevOps Engineer Nanodegree

Welcome to Udacity! We're excited to share more about your nanodegree and start this journey with you! In this course, you will learn more about the pre-requisites, structure of the program, and getting started!

Course 2 4 weeks

Clean Code Principles

Develop skills that are essential for deploying production machine learning models. First, you will put your coding best practices on auto-pilot by learning how to use PyLint and AutoPEP8. Then you will further expand your git and Github skills to work with teams. Finally, you will learn best practices associated with testing and logging used in production settings in order to ensure your models can stand the test of time.

Course 3 4 weeks

Building a Reproducible Model Workflow

This course empowers the students to be more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow. Along the way, it also touches on other technologies like Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class.

Course 4 4 weeks

ML Model Scoring and Monitoring

This course will help students automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. They will set up regular scoring processes to be performed after model deployment, and also learn to reason carefully about model drift, and whether models need to be retrained and re-deployed. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues. Finally, students will learn to set up automated reporting with API’s.

Taught By The Best

Photo of Giacomo Vianello

Giacomo Vianello

Principal Data Scientist

Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.

Photo of Ulrika Jägare

Ulrika Jägare

Head of AI/ML Strategy Execution in Ericsson

Ulrika has been with Ericsson for 21 years in various leadership roles, out of which 11 years in the Data and AI space. Ulrika holds a Master of Science degree from University of Lund in Sweden and is also author of seven published books in Data Science.

Photo of Justin Clifford Smith, PhD

Justin Clifford Smith, PhD

Senior Data Scientist at Optum

Justin a Senior Data Scientist at Optum where he works to make healthcare more efficient with natural language processing and machine learning. Previously he was a Data Scientist at the US Census Bureau. His doctorate is from the University of California, Irvine where he studied theoretical physics.

Photo of Bradford Tuckfield

Bradford Tuckfield

Data Scientist and Writer

Bradford Tuckfield is a data scientist and writer. He has worked on applications of data science in a variety of industries. He's the author of Dive Into Algorithms, forthcoming with No Starch Press.

Photo of Joshua Bernhard

Joshua Bernhard

DATA SCIENTIST AT THUMBTACK

Josh has been sharing his passion for data for nearly a decade at all levels of university, and as a Data Science Instructor for coding bootcamps. He's used data science for work ranging from cancer research to process automation.

Ratings & Reviews

Average Rating: 4.6 Stars

(87 Reviews)

Page 1 of 17

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

  • Gain proven experience

  • Retain knowledge longer

  • Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

  • Get help from subject matter experts

  • Learn industry best practices

  • Gain valuable insights and improve your skills