Nanodegree Program

Machine Learning DevOps Engineer

Up your game by streamlining the integration of machine-learning models and deploying them to a production-level environment.
Enroll Now
  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    4 Months

    At 10 hours/week

  • Enroll by
    November 3, 2021

    Get access to the classroom immediately upon enrollment

  • Prerequisites
    Prior experience with Python and Machine Learning

What You Will Learn


Machine Learning DevOps Engineer

In this program, you will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.

In this program, you will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.

Related Nanodegrees
Prerequisite Knowledge

Prior experience with Python and Machine Learning.

  • 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.

  • 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.

  • Deploying a Scalable ML Pipeline in Production

    This course teaches students how to robustly deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked, and auto-documented API using FastAPI.

  • Automated 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.

Icon - Dark upwards trend arrow
An estimated 87% of models fail to make it into production and team expertise is cited as one of the main reasons for that failure.

All Our Programs Include

Real-world projects from industry experts

Real-world projects from industry experts

With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Technical mentor support

Technical mentor support

Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Career Services

Career services

You’ll have access to resume support, Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program

Flexible learning program

Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program OfferingsFull list of offerings included:
Enrollment Includes:
Class Content
Real-world projects
Project reviews
Project feedback from experienced reviewers
Student Services
Technical mentor support
Student community
Career services
Resume support
Github review
LinkedIn profile optimization
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Get timely feedback on your projects
Reviews By the numbers
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
Mentors available to answer your questions
Mentors by the numbers
1,400+ technical mentors
0.85 hours median response time
Mentorship Services
  • Support for all your technical questions
  • Questions answered quickly by our team of technical mentors

Learn with the best

Joshua Bernhard
Joshua Bernhard


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.

Giacomo Vianello
Giacomo Vianello

Lead Data Scientist at Cape Analytics

Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Lead 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.

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.

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.

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.

Machine Learning DevOps Engineer

Get Started Today

  • Monthly Access

    Pay as you go




    Enroll Now
    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • - access

    Pay upfront and save an extra 0%

    for - access

    Enroll Now
    • Save an extra 0% vs. pay as you go.
    • 4 months is the average time to complete this course.
    • Switch to monthly price after if more time is needed.
    • Cancel anytime.
    Best Value
  • Learn

    Streamline the integration of machine-learning models and deploy them to a production-level environment.
  • Average Time

    On average, successful students take 4 months to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor support
    • Career services

Program Details

  • Why should I enroll?
    Data and AI professionals today are expected to be able to go beyond training ML models to packaging, deploying, and monitoring them in production environments. Whether you’re a Data Scientist, Data Engineer, Software Engineer, or any other role working with ML models, building this DevOps skillset will set you apart.
  • What jobs will this program prepare me for?
    The skills you build in this program will be instrumental in roles such as Data Scientist, Data Engineer, Machine Learning Engineer, DevOps Engineer, and beyond.

    ML DevOps is leveraged in a wide range of industries, from public transportation and healthcare to engineering, safety, and manufacturing. From models that automatically recognize certain types of medication to models that anticipate the effects of earthquakes, autonomous (and deployed!) systems yield real-world impact with the assistance of MLOps.
  • How do I know if this program is right for me?
    This course is for individuals who recognize the importance of machine learning model deployment but struggle to push the models they have developed in modeling environments to production to be self-functioning.
  • Do I need to apply? What are the admission criteria?
    No. This Nanodegree program accepts all applicants regardless of experience and specific background.
  • What are the prerequisites for enrollment?

    A well-prepared student will already be familiar with:

    • The data science process and overall workflow of building machine learning models
    • Using Jupyter notebooks to solve data science-related problems
    • Writing scripts using NumPy, pandas, Scikit-learn, TensorFlow/PyTorch in Jupyter notebooks that clean data (as part of ETL), feed it into a machine learning model and validate the performance of the model
    • Using the Terminal, version control in Git, and using GitHub
  • If I do not meet the requirements to enroll, what should I do?
    To prepare, we recommend the Introduction to Machine Learning and AI Programming with Python programs to build your comfortability with ML concepts and using python in an AI context.
  • How is this Nanodegree program structured?
    The Machine Learning DevOps Engineer Nanodegree program is comprised of content and curriculum to support four (4) projects. We estimate that students can complete the program in four (4) months working 10 hours per week.

    Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
  • How long is this Nanodegree program?
    Access to this Nanodegree program runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.
  • Can I switch my start date? Can I get a refund?
    Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
  • What software and versions will I need in this program?
    You will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.x and supporting packages.

    Most modern Windows, OS X, and Linux laptops or desktops will work well; we do not recommend a tablet since they typically have less computing power. We will provide you with instructions on how to install the required software packages. Additional tech requirements can be found at

Machine Learning DevOps Engineer

Enroll Now