Udacity Machine Learning DevOps Engineer Nanodegree Review

Udacity Machine Learning DevOps Engineer Nanodegree

Investing your time and money is serious business, so you need to know if a degree program is going to be worth it. In this review, we’re going to take a look at what you can get out of the course and what you need to put into it so that you can decide whether it’s the stepping stone your career needs.

Platform: Udacity.com

Duration: 4 months working around 10 hrs a week

Price: Monthly access from $399

Certificate: You will receive a certificate upon successful completion of the course

Level: Advanced

Overview of the Udacity Machine Learning DevOps Engineer Nanodegree

Machine learning is a field that is constantly expanding, as are the expectations for anyone who wants to succeed in the industry. It’s no longer enough to just understand how to train machine learning systems; you now need to be able to package, deploy, and monitor them in production environments too.

This Nanodegree is focussed on the fundamentals. It is carefully designed to give you all the skills you need to effectively build and manage an ML model.

You will learn how to implement Python code that is ready for production, monitor summaries and performance over time to prevent degradation, engineer automated workflows for data (with model validation and continuous training), and construct complex pipelines that include automated retraining and deployments.

Udacity’s Machine Learning DevOps Engineer Nanodegree Syllabus

1.    The Principles of Clean Code

The first course in the program focuses on how to actually deploy production ML models. You will learn how to work more efficiently and follow PEP8 standards, improve your Github skills by working in teams, and understand how to be sure that your code is production ready.

2.    How to Build Reproducible Model Workflows

In this course, you will become more productive, efficient, and effective in your machine learning projects. It teaches you the foundations of successful, reproducible ML pipelines, best practices for exploring and preparing data, how to track training, validation, and experiments, and how to ultimately deploy your code.

3.    Deploying a Scalable ML Pipeline in Production

This part of the program enables you to actually deploy an ML model to production. It includes performance testing and preparing for production, data and model versioning, CI/CD, and API deployment with FastAPI.

4.    Automated Scoring and Monitoring for Models

Once you have all the skills to deploy an efficient pipeline, you will then learn how to automate the process for scoring and re-deploying your ML models. You’ll set up scoring processes, understand model scoring and model drift, learn how to diagnose and fix operational issues, and report and monitor models with APIs.

Instructors of Udacity Machine Learning DevOps Engineer Nanodegree

1. Joshua Bernhard – Data Scientist at Thumbtack

Josh has been imparting data science wisdom at university levels and in coding boot camps for almost ten years. He has also implemented his own knowledge in a wide variety of areas from the automation of processes to cancer research.

2. Giacomo Vianello – Lead Data Scientist

Giacomo is the Lead Data Scientist for Cape Analytics, using AI systems in order to gather understanding from geospatial imagery and solve complex problems in the worlds of real estate and insurance.

3. Justin Smith, Ph.D. – Data Scientist

Justin works with Optum as a Data Scientist, improving the efficiency of healthcare services with machine learning and NLP. He also has experience working for the census bureau of the United States as a Data Scientist.

4. Bradford Tuckfield – Writer and Data Scientist

Bradford has worked in Data Science for many years and has written extensively in the field.

5. Ulrika Jägare – Head of AL/ML Strategy Execution in Ericsson

Ulrika has worked for Ericsson for more than two decades, 11 years of which have been in AI and Data. She has written and published seven books about Data Science and has an MSc.

Completion Time

Depending on how much time you commit to the course on a weekly basis, this program can be very quick to complete.

Most students manage to finish the Machine Learning DevOps Engineer Nanodegree in just 4 months, dedicating between 5 and 10 hours/week on average.


This course is open and available to anyone who is interested, and you do not need to have any level of qualification or experience to be accepted. With that being said, it is relatively advanced and would be challenging if you did not have at least some prior understanding before getting started.

The main prerequisite knowledge that you will need to be successful is a reasonable level of Python and machine learning experience. In order to be particularly well prepared for the course, you should be relatively familiar with:

  • The data science process and the basics of machine learning model building
  • Solving data science problems using Jupyter notebooks
  • Using TensorFlow/PyTorch, Scikit-Leaarn, pandas, and NumPy to write scripts 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 you don’t feel quite ready for a course of this level, you could instead start out with Udacity’s courses on AI Programming with Python and/or Introduction to Machine Learning.

Pros and Cons of “Udacity’s Machine Learning DevOps Engineer Nanodegree”


  • Available to all
  • Highly valuable skillset
  • Important knowledge for working in many careers, such as DevOps Engineers, Machine Learning Engineers, Data Scientists, and Data Engineers
  • Can be completed in 4 months
  • Reasonable monthly price


  • Challenging content that is best suited to people with a strong foundational understanding of both Python and Machine Learning
  • Can take longer to complete if you are unable to invest 10 hrs a week
  • Requires a computer with a reasonable level of computing power for running programs such as Anaconda and Python

Potential Job Roles

Machine Learning DevOps is a field that has become integral to a wide number of different jobs in data. This course could help you to stand out from the crowd or advance in your current position.

The roles most directly associated with Machine Learning DevOps skills include Data Engineer, Data Scientist, Machine Learning Engineer, DevOps Engineer, Machine Learning Ops, Cloud Engineer, and much more besides.

Is The Udacity Machine Learning DevOps Engineer Nanodegree Course Worth it?

Is the Udacity Machine Learning DevOps Engineer Nanodegree worth it? Absolutely. Like so many of the other courses that Udacity provides, you get a huge amount of value for your money and time with this course, which is why it has received nearly a full 5 stars from over 100 reviews.

Developing your ability to build and monitor machine learning models in a truly impactful way can make a huge difference to your career and open you up to significant opportunities.

We all know that the world of data and AI is constantly evolving, and this Nanodegree is a great way to make sure that you are staying ahead of the game rather than being left behind. Machine Learning DevOps as a skillset is establishing a key role in an increasing number of industries, having an impact on everything from more efficient public transport systems to improved earthquake preparedness.

What Else Do Nanodegrees Courses From Udacity Include?

Real-world projects from industry experts

These courses provide access to more than just theoretical knowledge and understanding. You will be given the opportunity to work on real-world projects and engage with immersive content that has been developed alongside some of the biggest names in the industry, providing you with the skills that high-level companies are looking for.

Technical mentor support

Many online courses can make you feel like you’re struggling by yourself but, with Udacity, you will always have an expert to talk to. Their mentors are highly knowledgeable and incredibly helpful, answering any questions you have and guiding you toward success.

Career services

You get a lot more than just the course content itself, too. You will also be able to access both Github portfolio review and LinkedIn profile optimization so that you can be sure you’re putting your best foot forward in your career.

Flexible learning program

Unlike traditional degree programs, you can adjust this course to fit around the rest of your life. Learn as fast or as slow as you need and put the time in when it suits you.

Summary: Udacity Machine Learning DevOps Engineer Nanodegree Review

So, what do we think of the Udacity Machine Learning DevOps Engineer Nanodegree? We think this course is an excellent way to get ahead in data and machine learning, which is a field that has been absolutely exploding over the last few years.

If you want to stand out to big tech companies and give yourself the skills to confidently pack, deploy, and monitor ML models in a production environment, then this course is a great way to do it.

It’s good value for money, it’s filled with high-level, actionable content, and you can complete the entire program in just 4 months.

Josh Hutcheson

E-Learning Specialist in Online Programs & Courses Linkedin

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