Getting Started with Amazon Personalize

In this course, you will learn how to use Amazon Personalize to build a personalized product experience for your customers. You will learn the basic terminology, important benefits and features, typical use cases, and costs. You will review the solution architecture you can use to build your own recommendation engines and personalization solutions. Through a guided tutorial, you will also build a movie recommendation engine solution in your own Amazon Web Services (AWS) account that you can further adapt to your use case.

This course is a part of the Getting Started series for Amazon Personalize (AWS console access required).

• Course Level: Fundamental
• Duration: 1 Hour 30 Minutes

Activities

This course includes presentations, readings, and demonstrations.

Course objectives

In this course, you will learn to:

• Describe the benefits of using Amazon Personalize to build recommendation systems and personalization solutions for your customers
• Describe the basic concepts, typical solution architectures, use cases, and costs associated with an Amazon Personalize solution
• Implement a demo in the AWS Management Console that shows Amazon Personalize in action

Prerequisites

We recommend that attendees of this course have:

• AWS Cloud Technical Essentials 

Course outline

Section 1

• What does Amazon Personalize do?
• What problem does Amazon Personalize solve?
• What are the benefits of Amazon Personalize?

Section 2

• How can Amazon Personalize help you architect a cloud solution?

• Architecture diagram
• Data layer
• Training layer
• Inference layer

Section 3

• What are typical use cases for Amazon Personalize?

Section 4

• What else should I keep in mind when using Amazon Personalize?

• Workflow options
• Workflow for domain dataset groups
• Workflow for custom dataset groups

Section 5

• How much does Amazon Personalize cost?

• Use-case optimized recommenders
• User segmentation
• Custom recommendation solutions

Section 6

• What are basic technical concepts I should know?

• Data import and management
• Training
• Model deployment and recommendations

• How does Amazon Personalize import your data, train a model, and generate recommendations?

• Architecture diagram
• Architecture explanation
• Estimated cost to run a demo (in user account)
• Steps for demo

• Create Amazon Simple Storage Service (Amazon S3) bucket and load sample data
• Import training data
• Clean up instructions
• Train a model and create recommender
• Get recommendations

• How can I learn more about Amazon Personalize? (links and resources)