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.
AWS Machine Learning Engineer Nanodegree
Kaggle , Amazon and Amazon Web Services via Udacity Nanodegree
Overview
Syllabus
- Welcome to AWS Machine Learning Engineer Nanodegree
- 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.
- 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.
- 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. - Operationalizing Machine Learning on SageMaker
- This course covers advanced topics related to deploying professional machine learning projects on SageMaker. Students will learn how to maximize output while decreasing costs. They will also learn how to deploy projects that can handle high traffic, how to work with especially large datasets, and how to approach security in machine learning AWS applications.
- Capstone Build Your Own Machine Learning Portfolio
- Congratulations!
- Congratulations on finishing your program!
- Career Services
Taught by
Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard , Jay Alammar, Andrew Paster, Chieko N., Mauricio B., Manuel F., Parag A., Bhavani C. and Nuttapong W.
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Reviews
4.8 rating, based on 11 Class Central reviews
4.7 rating at Udacity based on 46 ratings
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Good start for a machine learning course
Quality of the teaching videos is high and dialogue enjoyable. The project works are challenging and still developing as the course is new. The reviewers are professional and guide your work nicely. Some projects require a lot of work and maybe some improvement on the instructions. All in all I was very happy with this Nanodegree. -
Chandramohan NayakContent and Project Quality
I liked the projects ,and except for the content presentation for Reinforcement Learning most was presented extremely well and easily consumable. RL was slightly complicated to understand , and also I would have liked better mentor support on the Quad project. -
Good course for Machine Learning introduction
A complete, although somewhat superficial, course on machine learning fundamentals. It covers Supervised, Unsupervised and Reinforcement Learning. Now recently it also added Deep Learning. So it has all the fundamentals. The course content is goodl altough it could be better harmonized, because sometimes it feels (and it is) content from various courses all glued together, with hard transitions between content. All in all, it's a good degree. -
This Machine Learning Nanodegree program is great.
This program is going quite well. It is clear how the coursework applies to actual job skills.
One thing that made this amazing is the different project we are working on it. The submit project process is very easy and the response to it is quick and clear.
The mentor point the things that was well done and on the one that was not well done, he/her will explain and guide how you can solve it. -
until now I am satisfied with this nanodegree experience. The path is rich in information. I especially appreciate the opportunity to begin to know various aspects of the AWS cloud services, learning in the first place how to take care of costs, that any inexperienced people can face at the first approach. After completing this nanodegree, I am sure that I can be more confident in adopting AWS in my future work. Thanks
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Thank you Udacity
This was great experience and a lot of learning! Given constant support and follow up on project reviews along the course I felt sort of liability to perform well and excitement from learning my favorite subject. Consequtive topics selection and interesting midterm projects made it captivating! I mastered new techniques, got answers on my questions and leveled up with this course. -
Good all round support for students
ML Engineer - The course material is quite good with not too much focus on the mathematics. The project work helps one apply what was learned. Completing these projects increases the students confidence in being able to tackle new projects. The reviewers provide quick feedback with additional links that can help one improve their deliverable even further. -
The program covers the engineering part quite well and gives student opportunity to practice skills needed for a machine learning engineer, e.g. deploy a model, create an API, build a web application. I appreciate the superb resources given exclusively by Udacity and would be excited to finish the project.
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Perfect place to learn
In my practice-based opinion, Udacity is a perfect place to learn. They cover everything in great detail and in the simplest form possible. Perfect bang for the buck. :D -
I'm guessing the best complementary or continuation course of Deep Learning Nanodegree (also from Udacity), and both taken together, I think would be the best introduction for a ML Eng career :).
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One of the bests online, classes are greatly designed to make you learn in a simple, but growing dificulty, dealing with the real path to desing a machine learning pipeline
