Professional Level

Machine Learning on AWS Deep Dive

By Amber Israelsen

Deep dive into machine learning (ML) on AWS! Learn to build, train, and deploy ML projects with SageMaker Studio and other AWS services.

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4 hours

45 Lessons
5 Course Quizzes

About the course

In this course, Machine Learning on AWS Deep Dive, you’ll learn to securely build, train, and deploy machine learning (ML) projects with SageMaker Studio and other AWS technologies. First, you’ll explore the SageMaker suite of products and AWS AI/ML services. Next, you’ll discover how to use SageMaker Studio to prepare data and build, train, and deploy a machine learning model. Finally, you’ll learn how to apply security best practices to your machine learning pipelines. When you’re finished with this course, you’ll have the skills and knowledge of machine learning on AWS needed to solve real-world problems.

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Chapter 1 2 Lessons Introduction 17:06

Course Introduction
3:43

How to Follow Along with Demos in This Course
13:23
Chapter 2 11 Lessons Machine Learning on AWS: Tools and Services 1:17:44

Section Introduction
9:50

SageMaker Canvas Overview
2:58

Setting Up SageMaker Canvas
4:45

Running Predictions in SageMaker Canvas
14:09

SageMaker Studio Lab Overview
2:36

Setting Up a SageMaker Studio Lab Account
6:35

Exploring the SageMaker Studio Lab Examples
7:46

SageMaker Studio Overview
2:41

Setting Up SageMaker Studio for Course Demos
9:34

Section Summary
1:50

Quiz: Machine Learning on AWS: Tools and Services
15:00 Quiz
Chapter 3 9 Lessons Preparing Data with SageMaker Data Wrangler and Feature Store 1:32:30

Section Introduction
4:31

Importing Data into Data Wrangler
13:48

Analyzing and Visualizing Data in Data Wrangler
12:34

Transforming Data in Data Wrangler
14:56

Exporting Data for Training
10:59

Working with the SageMaker Feature Store
4:40

Ingesting and Accessing Features in SageMaker Feature Store
15:01

Section Summary
1:01

Quiz: Preparing Data with SageMaker Data Wrangler and Feature Store
15:00 Quiz
Chapter 4 10 Lessons Building and Training Models with SageMaker Studio 1:30:14

Section Introduction
2:54

Using SageMaker's Built-In Algorithms
9:17

SageMaker's Algorithms in Action
15:01

Using Popular Frameworks
5:42

Working in Script Mode with a Framework
9:46

Using SageMaker Experiments to Manage Training Jobs
11:05

Detecting Bias with SageMaker Clarify
13:10

Using SageMaker for Collaboration
6:11

Section Summary
2:08

Quiz: Building and Training Models with SageMaker Studio
15:00 Quiz
Chapter 5 6 Lessons Deployment and Inference with SageMaker Studio 42:24

Section Introduction
3:04

Working with SageMaker Batch Transform
8:02

Hosting Real-Time Endpoints for Live Inference
9:22

Optimizing Machine Learning Deployments
5:36

Section Summary
1:20

Quiz: Deployment and Inference with SageMaker Studio
15:00 Quiz
Chapter 6 10 Lessons Security Best Practices for Machine Learning on AWS 48:33

Section Introduction
2:51

Securing Your Resources in a VPC
5:08

Using the Principle of Least Privilege
4:11

Encrypting Data at Rest and in Transit
8:12

Protecting Credentials with Secrets Manager
3:28

Monitoring Model Input and Output
1:19

Enabling Logging for Model Access
2:55

Using Version Control on Model Artifacts
3:39

Section Summary
1:50

Quiz: Security Best Practices for Machine Learning on AWS
15:00 Quiz
Chapter 7 2 Lessons Conclusion 7:02

Course Summary
4:56

Conclusion and What's Next
2:06

Practice alongside courses in Cloud Playground

What is Cloud Playground? Cloud Playground lets you build skills in real-world AWS, Google Cloud, and Azure environments. Spin up risk-free Sandboxes, Servers and Terminals and follow along with courses, test a new idea or prepare for exams.

Machine Learning on AWS Deep Dive

About the course

Course Introduction

How to Follow Along with Demos in This Course

Section Introduction

SageMaker Canvas Overview

Setting Up SageMaker Canvas

Running Predictions in SageMaker Canvas

SageMaker Studio Lab Overview

Setting Up a SageMaker Studio Lab Account

Exploring the SageMaker Studio Lab Examples

SageMaker Studio Overview

Setting Up SageMaker Studio for Course Demos

Section Summary

Quiz: Machine Learning on AWS: Tools and Services

Section Introduction

Importing Data into Data Wrangler

Analyzing and Visualizing Data in Data Wrangler

Transforming Data in Data Wrangler

Exporting Data for Training

Working with the SageMaker Feature Store

Ingesting and Accessing Features in SageMaker Feature Store

Section Summary

Quiz: Preparing Data with SageMaker Data Wrangler and Feature Store

Section Introduction

Using SageMaker's Built-In Algorithms

SageMaker's Algorithms in Action

Using Popular Frameworks

Working in Script Mode with a Framework

Using SageMaker Experiments to Manage Training Jobs

Detecting Bias with SageMaker Clarify

Using SageMaker for Collaboration

Section Summary

Quiz: Building and Training Models with SageMaker Studio

Section Introduction

Working with SageMaker Batch Transform

Hosting Real-Time Endpoints for Live Inference

Optimizing Machine Learning Deployments

Section Summary

Quiz: Deployment and Inference with SageMaker Studio

Section Introduction

Securing Your Resources in a VPC

Using the Principle of Least Privilege

Encrypting Data at Rest and in Transit

Protecting Credentials with Secrets Manager

Monitoring Model Input and Output

Enabling Logging for Model Access

Using Version Control on Model Artifacts

Section Summary

Quiz: Security Best Practices for Machine Learning on AWS

Course Summary

Conclusion and What's Next

Practice alongside courses in Cloud Playground

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