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Course Introduction

The course is part of these learning paths

Solutions Architect – Professional Certification Preparation for AWS
course-steps 47 certification 6 lab-steps 19 quiz-steps 4 description 2
Applying Machine Learning and AI Services on AWS
course-steps 5 certification 1 lab-steps 2

Contents

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Amazon Rekognition Course Review
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Course Introduction
Overview
Transcript
DifficultyBeginner
Duration1h 11m
Students371
Ratings
4.8/5
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Description

Overview

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.

Lectures

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.

Demonstrations

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

Pre-requisites

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

Feedback

If you have thoughts or suggestions for this course, please contact Cloud Academy at support@cloudacademy.com.

About the Author

Students8788
Labs26
Courses58
Learning paths13

Jeremy is the DevOps Content Lead at Cloud Academy where he specializes in developing technical training documentation for DevOps.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 20+ years. In recent times, Jeremy has been focused on DevOps, Cloud, Security, and Machine Learning.

Jeremy holds professional certifications for both the AWS and GCP cloud platforms.
 

- [Jeremy] Hello and welcome to this Cloud Academy course on Amazon Rekgnition. In this first lecture, we'll cover off the course agenda, intended audience, learning objectives, and course prerequisites. Before we start, I would like to introduce myself. My name is Jeremy Cook, I'm one of the trainers here at Cloud Academy, specializing in AWS. Feel free to connect with either myself or the wider team here at Cloud Academy, regarding anything about this course. You can email us at support@cloudacademy.com, or alternatively, our online community forum is available for your feedback.

This training course begins with an introduction of computer vision, and proceeds with the concepts of image and video analysis. We'll then briefly discuss the reasons as to where and when you might consider performing image and video analysis using Amazon Rekognition. 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'll continue on in the course, providing in-depth reviews of the image, video and collection-based API sets.

We then finish the course with a couple of demonstrations, the first of which 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 second demonstration we will show you how to use the AWS CLI to implement an object and feature detection system that sends and email when certain features are detected within an image. Both demonstrations will highlight the capabilities of Amazon Rekognition and the different approaches you can adopt to interface with the service.

The intended audience for this course includes: Data Scientists, interested in mining information from images and/or videos. Machine Learning Enthusiasts with an interest in computer vision. Developers interested in learning how to integrate image and video analysis in to their own applications. And anyone interested in learning how Amazon Rekognition works.

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 in the storgage-based API set, understand business use cases and scenarios that can benefit from using the Amazon Rekognition service, and finally, be able to architect and integrate Amazon Rekognition in to your own applications.

The agenda for the remainder of this course is as follows: We will 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 utilize the Rekognition service. And finally, we'll present two demonstrations that highlight how to integrate with the Rekognition service.

The following prerequisites will be both useful and helpful for this course. General development and coding experience, AWS, S3 and IAM security experience. Okay, the course introduction has now been completed, go ahead and close this lecture, and we'll see you shortly in the next one, where we'll begin discussing the Rekognition service.