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
AWS Machine Learning Engineer Nanodegree
Nanodegree Program
The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.
The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.
Built in collaboration with
AWS
Intermediate
4 months
Real-world Projects
Completion Certificate
Last Updated July 29, 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
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.
Course 2 • 4 weeks
Introduction to Machine Learning
In this course, you'll start learning what machine learning is by being introduced to the high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.
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.
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.
Taught By The Best
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.
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.
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.
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.
Joseph Nicolls
Senior Machine Learning Engineer - Blue Hexagon
Joseph Nicolls is a senior machine learning scientist at Blue Hexagon. With a major in Biomedical Computation from Stanford University, he currently utilizes machine learning to build malware-detecting solutions at Blue Hexagon.
Ratings & Reviews
Average Rating: 4.35 Stars
42 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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2 months
, Intermediate
(235)
2 months
, Intermediate
(912)
3 months
, Intermediate
(1)
2 months
, Advanced
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2 months
, Advanced
(82)
1 month
, Intermediate
(398)
3 months
, Intermediate
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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.