Machine Learning DevOps Engineer

Name: Machine Learning DevOps Engineer Nanodegree Program
Rating: 4.6 (99 reviews)

Nanodegree Program

Automate and streamline the deployment of machine learning models, combining software engineering principles with machine learning workflows. This program covers key skills like writing production-ready code, creating reproducible workflows, and building automated deployment pipelines. Through real-world projects, you’ll develop scalable pipelines, version control for data and models, monitor model performance, and implement CI/CD processes to ensure resilient, maintainable systems. Ideal for data scientists aiming to operationalize their models, these skills empower you to build effective, long-term solutions tailored for industry demands.

Advanced
3 months
Last Updated November 21, 2024

Skills you'll learn:

GitMachine learning ops troubleshootingDvc

Prerequisites:

Command line interface basicsBasic descriptive statisticsJupyter notebooksBasic machine learningIntermediate PythonREST APIs

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.

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 • 3 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!

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

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!

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 • 2 weeks

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.

Lesson 1

Introduction to Deploying a Scalable ML Pipeline in Production

We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.

Lesson 2

Performance Testing and Preparing a Model for Production

In this lesson, we will cover performance testing and preparing a model for production.

Lesson 3

[Optional] Data and Model Versioning

In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.

Lesson 4

CI/CD

We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.

Lesson 5

API Deployment with FastAPI

Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live.

Lesson 6 • Project

Deploying a ML Model to Cloud Application Platform with FastAPI

Lesson 1

Introduction to Deploying a Scalable ML Pipeline in Production

We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.

Lesson 2

Performance Testing and Preparing a Model for Production

In this lesson, we will cover performance testing and preparing a model for production.

Lesson 3

[Optional] Data and Model Versioning

In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.

Lesson 4

CI/CD

We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.

Lesson 5

API Deployment with FastAPI

Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live.

Lesson 6 • Project

Deploying a ML Model to Cloud Application Platform with FastAPI

Taught By The Best

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.

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.

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

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.

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

99 Reviews

VINAY S.

February 9, 2023

Great. Learnt a lot on writing modular code

ÌïòÎ¶º(Harim) Ïû•(Jang)

December 24, 2022

Great help! Thanks!

Abdulrasheed A.

September 6, 2022

nice to get going

Abderrezak A.

August 15, 2022

Good help

Zanuar E.

August 14, 2022

It is matched my needs and expectations

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.