Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
How to Become a Machine Learning Engineer
Nanodegree Program
Meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights.
04Days07Hrs09Min50Sec
Estimated time
5 Months
At 5-10 hours/week
Enroll by
March 29, 2023
Get access to the classroom immediately upon enrollment
Skills acquired
Neural Networks and Deep Learning, AWS Lambda, Amazon SageMaker
In collaboration with
What you will learn
AWS Machine Learning Engineer
Estimated 5 months to completeYou’ll master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker.
Prerequisite knowledge
Basic knowledge of machine learning algorithms and Python programming.
Introduction to Machine Learning
In this course, you'll start learning about machine learning through 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.
Developing Your First ML Workflow
In this course you will learn how to create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline.
Deep Learning Topics within 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 which tools are 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.
Operationalizing Machine Learning Projects on SageMaker
This course covers advanced topics related to deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs. You will also learn how to deploy projects that can handle high traffic and how to work with especially large datasets.
CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers
Distribution centers often use robots to move objects as a part of their operations. Objects are carried in bins where each bin can contain multiple objects. In this project, students will have to build a model that can count the number of objects in each bin. A system like this can be used to track inventory and make sure that delivery consignments have the correct number of items.
To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.
All our programs include
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Career services
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program offerings
Class content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community
Career services
- Github review
- LinkedIn profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
- Experienced Project Reviewers
- Technical Mentor Support
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Learn with the best.
Learn with the best.
Matt Maybeno
Principal Software Engineer - Data Science and Machine LearningMatt Maybeno 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.
Joseph Nicolls
Senior Machine Learning Engineer - Blue HexagonJoseph 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.
Charles Landau
Technical Lead, AI/ML - GuidehouseCharles 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.
Soham Chatterjee
GRADUATE STUDENT AT THE NANYANG TECHNOLOGICAL UNIVERSITYSoham 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.
Bradford Tuckfield
Independent ConsultantBradford does independent consulting for machine learning projects related to manufacturing, law, pharmaceutical operations, and other fields. He also writes technical books about programming, algorithms, and data science.
AWS Machine Learning Engineer
Get started today
Learn
Amazon SageMaker best practices, including new model design and deployment features and case studies to which they can be applied.
Average Time
On average, successful students take 5 months to complete this program.
Benefits include
- Real-world projects from industry experts
- Technical mentor support
- Career services
Program details
Program overview: Why should I take this program?
Why should I enroll?
This program is designed to help you take advantage of the growing need for skilled machine learning professionals. Prepare to meet the demand for qualified engineers that can build and deploy machine learning models in production.
What jobs will this program prepare me for?
The skills you will gain from this Nanodegree program will qualify you for jobs in several industries as countless companies are trying to incorporate machine learning into their practices.
How do I know if this program is right for me?
The course is for individuals who are looking to advance their engineering careers with cutting-edge machine learning skills.
Enrollment and admission
Do I need to apply? What are the admission criteria?
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
What are the prerequisites for enrollment?
A well prepared student will be familiar with Python programming knowledge, including:
- At least 40 hours of programming experience
- Familiarity with data structures like dictionaries and lists
- Experience with libraries like NumPy and pandas
- Knowledge of functions, variables, loops, and classes
- Exposure to Python through Jupyter Notebooks is recommended
- Experience with constructing and calling HTTP API endpoints is recommended
Basic knowledge of machine learning algorithms, including:
- Basic understanding of the machine learning workflow
- Basic theoretical understanding of ML algorithms such as linear regression, logistic regression, neural network
- Basic understanding of model training and testing processes
- Basic knowledge of commonly used metrics for ML models evaluation such as accuracy, precision, recall, and mean square error (MSE)
If I do not meet the requirements to enroll, what should I do?
Students who do not feel comfortable in the above may consider taking Udacity’s Introduction to Programming or Intermediate Python to obtain prerequisite skills.
Tuition and term of program
How is this Nanodegree program structured?
The AWS Machine Learning Engineer Nanodegree program consists of content and curriculum to support five projects. We estimate that students can complete the program in five months working 5-10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
How long is this Nanodegree program?
Access to this Nanodegree program runs for the length of time specified above. If you do not graduate within that time period, you will continue learning with month-to-month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.
Can I switch my start date? Can I get a refund?
Please see the Udacity Program FAQs for policies on enrollment in our programs.
Software and hardware: What do I need for this program?
What software versions will I need in this program?
There are no software and version requirements to complete this Nanodegree program. All coursework and projects can be completed via Student Workspaces in the Udacity online classroom.