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.
Overview
Syllabus
- Welcome to AWS Machine Learning Engineer Nanodegree
- 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.
- 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
nd189 Matt Maybeno, nd189 Joseph Nicolls, nd189 Charles Landau, nd189 Soham Chatterjee and nd189 Bradford Tuckfield
