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AWS Machine Learning Engineer Nanodegree

Nanodegree Program

Develop the skills to build and deploy machine learning models in production environments using Amazon SageMaker. Understand the foundations of data science and machine learning as you explore Amazon SageMaker's latest features for model design and deployment. Engage in hands-on projects and case studies that mirror real-world scenarios, applying best practices to create scalable, production-ready machine learning solutions. Enhance your expertise in areas such as data preprocessing, model training, and deployment pipelines. Learn to leverage Amazon SageMaker's capabilities, including its integrated development environment, automated model tuning, and deployment services. This program is designed to advance your career by providing practical experience in deploying ML models at scale.

Develop the skills to build and deploy machine learning models in production environments using Amazon SageMaker. Understand the foundations of data science and machine learning as you explore Amazon SageMaker's latest features for model design and deployment. Engage in hands-on projects and case studies that mirror real-world scenarios, applying best practices to create scalable, production-ready machine learning solutions. Enhance your expertise in areas such as data preprocessing, model training, and deployment pipelines. Learn to leverage Amazon SageMaker's capabilities, including its integrated development environment, automated model tuning, and deployment services. This program is designed to advance your career by providing practical experience in deploying ML models at scale.

Built in collaboration with

AWS

  • Intermediate

  • 4 months

  • Last Updated December 18, 2024

Skills you'll learn:

Neural network basicsDeep learning fluency

Prerequisites:

AWS familiarityIntermediate PythonAPI proficiencyJupyter notebooksDeep learning

Intermediate

4 months

Last Updated December 18, 2024

Skills you'll learn:

Neural network basics • Deep learning fluency • Sagemaker jumpstart • Machine learning framework fundamentals

Prerequisites:

AWS familiarity • Intermediate Python • API proficiency

Skills that demand high salaries

Machine Learning Engineer

Machine Learning will grow by 40%, according to the World Economic Forum’s 2023 Future of Jobs Report. That’s the largest growth of any occupation.*

Salary Ranges

Low
$130,000
Average
$160,711
High
$208,590
Salary info from Talent.com

Courses In This Program

Course 1 • 45 minutes

Welcome to AWS Machine Learning Engineer Nanodegree

Lesson 1

An Introduction to Your Nanodegree Program

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 Your Nanodegree Program

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

Introduction to Machine Learning

Gain foundational machine learning expertise using AWS SageMaker, from data preparation and exploratory analysis to deploying powerful models like XGBoost and AutoGluon. Covering the entire ML workflow, including feature engineering, model tuning, and evaluation, you'll gain practical experience with real-world data projects. Designed for those familiar with Python and data science basics, the curriculum equips you to handle diverse ML tasks confidently and effectively, making it ideal for anyone looking to apply machine learning in various professional and industry settings.

Lesson 1

Introduction to Machine Learning

Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.

Lesson 2

Exploratory Data Analysis

Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.

Lesson 3

Machine Learning Concepts

In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.

Lesson 4

Model Deployment Workflow

In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.

Lesson 5

Algorithms and Tools

In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart

Lesson 6 • Project

Predict Bike Sharing Demand with AutoGluon

Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!

Lesson 1

Introduction to Machine Learning

Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.

Lesson 2

Exploratory Data Analysis

Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.

Lesson 3

Machine Learning Concepts

In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.

Lesson 4

Model Deployment Workflow

In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.

Lesson 5

Algorithms and Tools

In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart

Lesson 6 • Project

Predict Bike Sharing Demand with AutoGluon

Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!

Course 3 • 3 weeks

Developing your First ML Workflow

This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.

Lesson 1

Introduction to Developing ML Workflows

This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.

Lesson 2

SageMaker Essentials

This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.

Lesson 3

Designing Your First Workflow

This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.

Lesson 4

Monitoring a ML Workflow

This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.

Lesson 5 • Project

Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker

In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.

Lesson 1

Introduction to Developing ML Workflows

This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.

Lesson 2

SageMaker Essentials

This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.

Lesson 3

Designing Your First Workflow

This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.

Lesson 4

Monitoring a ML Workflow

This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.

Lesson 5 • Project

Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker

In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.

Course 4 • 3 weeks

Deep Learning Topics with Computer Vision and NLP

In this course you will learn how to train, finetune and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and the tools used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

Lesson 1

Introduction to Deep Learning Topics within Computer Vision and NLP

In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.

Lesson 2

Introduction to Deep Learning

In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.

Lesson 3

Common Model Architecture Types and Fine-Tuning

In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.

Lesson 4

Deploy Deep Learning Models on SageMaker

In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.

Lesson 5 • Project

Image Classification using AWS SageMaker

In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.

Lesson 1

Introduction to Deep Learning Topics within Computer Vision and NLP

In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.

Lesson 2

Introduction to Deep Learning

In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.

Lesson 3

Common Model Architecture Types and Fine-Tuning

In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.

Lesson 4

Deploy Deep Learning Models on SageMaker

In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.

Lesson 5 • Project

Image Classification using AWS SageMaker

In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.

Taught By The Best

Photo of Matt Maybeno

Matt Maybeno

Principal Software Engineer

Matt is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

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 Soham Chatterjee

Soham Chatterjee

GRADUATE STUDENT AT THE NANYANG TECHNOLOGICAL UNIVERSITY

Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

Photo of Charles Landau

Charles Landau

Technical Lead, AI/ML - Guidehouse

Charles holds a MPA from George Washington University, where he focused on econometrics and regulatory policy, and holds a BA from Boston University. At Guidehouse, he supports data scientists and developers working on internal and client-facing ML platforms.

Photo of Joseph Nicolls

Joseph Nicolls

Lead Engineer, ML/AI

Joseph has a bachelor’s degree in Biomedical Computation from Stanford University and over 5 years of technical experience in developing scalable machine learning workflows for cybersecurity use cases. In his free time, he enjoys reading with his wife and jogging by the San Francisco Bay.

Student Reviews

Average Rating: 4.7 Stars

47 Reviews

Shadman A.

February 3, 2023

This program is up to date and provide very good projects to practice the learned skills

Lamiaa H.

October 30, 2022

Thanks.

Subhasish S.

October 26, 2022

So far so good, although I was already familiar with the concepts covered so far. The coming concepts are what I'm most excited to learn about.

Guangchu Y.

September 7, 2022

Very good!

Jinwook B.

August 27, 2022

very up to date and excellent quality

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Your subscription also includes:

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About AWS Machine Learning Engineer Nanodegree

Our AWS Machine Learning Engineer Nanodegree program, built in collaboration with AWS, is an intermediate-level machine learning engineering course. It's designed to equip you with the skills needed to build and deploy machine learning models using Amazon SageMaker. The program covers neural network basics, deep learning fluency, and essential machine learning framework fundamentals. You'll learn through practical courses, including developing your first ML workflow and exploring deep learning topics with computer vision and NLP. At Udacity, we provide an unparalleled learning experience, combining expert instruction with real-world projects that ensure you can apply your skills immediately. Under the guidance of industry professionals like Matt Maybeno, you'll gain hands-on experience in AWS machine learning, preparing you to excel as an AWS machine learning engineer.

*https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf(opens in a new tab)

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