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.
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
5 months
Real-world Projects
Completion Certificate
Last Updated April 21, 2024
Course 1 • 45 minutes
Course 2 • 4 weeks
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.
Course 3 • 4 weeks
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.
Course 4 • 4 weeks
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.
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.
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.
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.
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.
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.
Average Rating: 4.7 Stars
(41 Reviews)
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
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.
Average time to complete a Nanodegree program
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.