Amazon SageMaker: Simplifying Machine Learning Application Development (edX)

This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.

Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises* as we explore this complex topic from the lens of the application developer.

*Note that there may be a cost associated with some exercises. If you do not wish to incur additional expenses, you may view demonstrations instead.

What you'll learn

- Key problems that Machine Learning can address and ultimately help solve

- How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance

- How to publish a model using Amazon SageMaker

- How to integrate the published SageMaker endpoint with an application

Syllabus

Welcome to Machine Learning with Amazon SageMaker

Course Introduction

Welcome to Machine Learning with SageMaker on AWS

Course Welcome and Student Information

Meet the Instructors

Introduce Yourself

Week 1

Introduction to Machine Learning with SageMaker on AWS

Introduction to Week 1

What we we use ML for?

Diving Right In

What is Amazon SageMaker

Weekly Quiz, Readings, Resources, Discussion

Week 1 Notes and Resources

Week 1 Quiz

Week 1 Discussion

Week 2

Amazon SageMaker Notebooks and SDK

Introduction to Week 2

Amazon SageMaker Notebooks

Introduction to Jupyter Notebooks

Notebooks and Libraries: Cleaning and Preparing Data

Exercise 2.1 Walkthrough

Exercise 2.1: Create Your Notebook Instance (Optional)

Weekly Quiz, Readings, Resources, Discussion

Week 2 Notes and Resources

Week 2 Quiz

Week 2 Discussion

Week 3

Amazon SageMaker Algorithms

Introduction to Week 3

ML and Amazon SageMaker Terminology

SageMaker/ML Terminology and Algorithms

Hyperparameter Tuning

Amazon SageMaker Algorithms

k-means Algorithm Walkthrough

Introduction to Exercise 3.1

Exercise 3.1: Using the k-means Algorithm (Optional)

XGBoost Algorithm Walkthrough (Part 1)

XGBoost Algorithm Walkthrough (Part 2)

XGBoost Algorithm Walkthrough (Part 3)

Introduction to Exercise 3.2

Exercise 3.2: Using the XGBoost Algorithm (Optional)

Weekly Quiz, Readings, Resources, Discussion

Week 3 Notes and Resources

Week 3 Quiz

Week 3 Discussion

Week 4

Application Integration

Introduction to Week 4

Integrating Amazon SageMaker with your Applications

Serverless Recap

Exercise 4.1 Walkthrough

Exercise 4.1: Python Movie Recommender (Optional)

Bring Your Own Models

Bringing Your Own Models: MXNet and TensorFlow

Weekly Quiz, Readings, Resources, Discussion

Week 4 Notes and Resources

Week 4 Quiz

Class Wrap Up

Course Survey

Week 4 Discussion

End of Course Assessment (Verified Certificate Track Only)

Prerequisites:

Prior application development experience

Experience with the AWS Console

Recommended: AWS Developer Professional Series , (Building on AWS, Deploying on AWS, Optimizing on AWS)