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!

Lesson 1

An Introduction to Machine Learning DevOps Engineer

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

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.

Lesson 1

Introduction

Get introduced to clean code principles, why and when to use them, and the history of clean code. Then, see what you'll be able to build by the end of the course!

Lesson 2

Coding Best Practices

Learn coding best practices, such as clean and modular code, code efficiency, refactoring, documentation, and linting.

Lesson 3

Working with Others Using Version Control

Version control is crucial for any coding project, but becomes even more important when working in teams. Another new area in working with teams is the code review, which you'll also learn about here.

Lesson 4

Production Ready Code

Find more coding best practices here, such as handling errors, testing and logging, as well as addressing model drift in machine learning models.

Lesson 5 • Project

Predict Customer Churn with Clean Code

Take a colleague's messy juypter notebook for building a customer churn prediction model and implement all of the clean code principles you have learned throughout the course!

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.

Lesson 1

Introduction to Reproducible Model Workflows

Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.

Lesson 2

Machine Learning Pipelines

Build out machine learning pipelines, as well as learning how to version data and model artifacts.

Lesson 3

Data Exploration and Preparation

Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.

Lesson 4

Data Validation

Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.

Lesson 5

Training, Validation and Experiment Tracking

Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.

Lesson 6

Final Pipeline, Release and Deploy

Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.

Lesson 7 • Project

Build an ML Pipeline for Short-term Rental Prices in NYC

Create a re-usable end-to-end pipeline for predicting short-term rental prices in New York City!

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.

Lesson 1

Welcome to ML Model Scoring and Monitoring

This lesson will talk about goals for the course, when to use ML model scoring and monitoring, stakeholders, the history of the field, and the tools and dependencies you need to be aware of.

Lesson 2

Model Training and Deployment

This lesson will talk about model training and deployment. We’ll focus on automating the training and deployment process and making sure that the trained, deployed models are ready to be monitored.

Lesson 3

Model Scoring and Model Drift

This lesson will discuss model scoring and model drift, an important part of the continuous monitoring that makes sure your deployed model remains as accurate as possible.

Lesson 4

Diagnosing and Fixing Operational Problems

There are problems that can come up in deployed projects. So this lesson will talk about diagnosing and fixing operational problems, a crucial part of the post-deployment machine learning process.

Lesson 5

Model Reporting and Monitoring with API's

This lesson will discuss model reporting and monitoring with APIs which can be used as an automatic interface with your ML project.

Lesson 6 • Project

Project: A Dynamic Risk Assessment System

The final project for this course will be a dynamic risk assessment system in which you will build and monitor an ML model to predict attrition risk.

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

Unlock access to Machine Learning DevOps Engineer and the rest of our best-in-class catalog

  • Unlimited access to our top-rated courses

  • Real-world projects

  • Personalized project reviews

  • Program certificates

  • Proven career outcomes

Full Catalog Access

One subscription opens up this course and our entire catalog of projects and skills.

Month-To-Month

4 Months

Average time to complete a Nanodegree program

*Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.

Your subscription also includes:

Get Started Today

Machine Learning DevOps Engineer

Month-To-Month


  • Unlimited access to our top-rated courses
  • Real-world projects
  • Personalized project reviews
  • Program certificates
  • Proven career outcomes

4 Months

Average time to complete a Nanodegree program

  • All the same great benefits in our month-to-month plan
  • Most cost-effective way to acquire a new set of skills
Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.

Related Programs

Udacity Logo
Company
  • Facebook
  • Twitter
  • LinkedIn
  • Instagram

© 2011-2024 Udacity, Inc. "Nanodegree" is a registered trademark of Udacity. © 2011-2024 Udacity, Inc.
We use cookies and other data collection technologies to provide the best experience for our customers.