Amazon Rekognition Concepts
Course Introduction5m 13s
Amazon Rekognition5m 13s
Image Processing - API5m 13s
Video Processing - API5m 13s
Collections - Storage Based API Operations5m 13s
Console, SDKs, and CLI5m 13s
Use Cases and Scenarios5m 13s
Amazon Rekognition Demonstrations
Demonstration - Facial Analysis Web App5m 13s
Demonstration - Feature Extraction - AWSCLI5m 13s
Amazon Rekognition Course Review
Course Review5m 13s

Start course

Duration52m 10s



In this course you'll learn about Amazon Rekognition, a service that enables you to easily and quickly integrate computer vision features directly into your own applications. At its core, Amazon Rekognition provides an API that you submit images and/or videos to. You then instruct the Rekognition service to perform a specific analysis on the media. The analysis can be anything from detecting faces within an image to extracting labels from a video in an asynchronous manner.


We’ll provide in-depth reviews of the image, video, and collection based API sets. Amazon Rekognition makes it easy to add image and video analysis to your applications. You simply submit your image or video to the Rekognition service, and the service will then identify objects, people, text, scenes, emotions, and activities. Additionally the service can be used to moderate content, by detecting inappropriate or objectionable content.


We finish the course with a couple of demonstrations:

  • In the 1st demonstration we will show you how to use Amazon Rekognition together with S3 and CloudFront for hosting, to build a simple facial analysis static web application.
  • In the 2nd demonstration we will show you how to use the AWSCLI to implement an object and feature detection system that sends an email when certain features are detected within an image.

Both demonstrations will highlight the capabilities of the Amazon Rekognition and the different approaches you can adopt to interface with the service.

Intended Audience

The intended audience for this course includes:

  • Data scientists interested in mining information from images and/or video
  • Machine Learning enthusiasts with an interest in computer vision
  • Developers interested in learning how to integrate image and video analysis into their own applications
  • Anyone interested in learning how Amazon Rekognition works

Learning Objectives

By completing this course, you will: 

  • Understand what Amazon Rekognition is and what it offers
  • Understand the benefits of using the Amazon Rekognition service
  • Understand how to use Amazon Rekognition APIs to process both images and videos
  • Understand how to use Collections and the storage based API set
  • Understand business use cases and scenarios that can benefit from using the Amazon Rekognition service
  • Be able to architect and integrate Amazon Rekognition into your own applications


The following prerequisites will be both useful and helpful for this course:

  • General development and coding experience
  • AWS S3 and IAM security experience (for the demonstrations)

Course Agenda

The agenda for the remainder of this course is as follows:

  • We’ll discuss what Amazon Rekognition is and when and why you might consider using it
  • We’ll review the Amazon Rekognition service and provide an in-depth review of each of its features
  • We’ll discuss benefits and business use cases that can be empowered by leveraging Amazon Rekognition
  • We’ll provide some example business use cases and scenarios that utilise the Rekognition service
  • Finally - We’ll present 2 demonstrations that highlight how to integrate with the Rekognition service


We welcome all feedback so please direct any comments or questions on this course to us at

About the Author

Learning paths2

Jeremy is a Cloud Researcher and Trainer at Cloud Academy where he specializes in developing technical training documentation for security, AI, and machine learning for both AWS and GCP cloud platforms.

He has a strong background in development and coding, and has been hacking with various languages, frameworks, and systems for the past 20+ years.

In recent times, Jeremy has been focused on Cloud, Security, AI, Machine Learning, DevOps, Infrastructure as Code, and CICD.

Jeremy holds professional certifications for both AWS and GCP platforms.

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