Udacity’s Machine Learning DevOps Engineer Nanodegree Program – Ratings and Review!

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In Udacity’s Machine Learning DevOps Engineer Nanodegree Program, up your game by streamlining the integration of machine-learning models and deploying them to a production-level environment.

Udacity's Machine Learning DevOps Engineer Nanodegree Program - Ratings and Review!

Enroll in Udacity’s Machine Learning DevOps Engineer Nanodegree Program today!

Overview of the Udacity’s Machine Learning DevOps Engineer Nanodegree Program

In Udacity’s Machine Learning DevOps Engineer Nanodegree Program, you will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.

Udacity’s Machine Learning DevOps Engineer Nanodegree Program Syllabus

1. 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.

2. 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.

3. 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.

4. 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.

Instructors of Udacity’s Machine Learning DevOps Engineer Nanodegree Program

Here is a list of instructors associated with the Udacity Nanodegree Program:

1. 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.

2. 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.

3. Justin Clifford Smith, PhD (Senior Data Scientist at Optum)

Justin is 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.

4. 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.

5. 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.

Review: “Definitely worth enrolling in!”

After successfully completing the Nanodegree program, you will find that the program is very comprehensive and teaches you everything promised by the program.

Plus, the certification is also really helpful since this Udacity Nanodegree Program is well-recognized amongst multiple industries.

Enroll in Udacity’s Machine Learning DevOps Engineer Nanodegree Program today!

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