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Amazon Rekognition

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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Amazon Rekognition
Overview
Transcript
DifficultyBeginner
Duration1h 11m
Students367
Ratings
4.8/5
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Description

In this lecture we'll now start diving into the Amazon Rekognition service and how it works. We discuss what motivations may exist that could require you to consider using the Amazon Rekognition service. We provide a brief understanding of how Rekognition uses machine learning technology under the hood. We'll discuss how the Amazon Rekognition API is structured into operations specific to image processing and those that are specific to video processing.

About the Author

Students8648
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.
 

- Welcome back. In this lecture, we'll now start diving into the Amazon Rekognition Service and how it works. Okay, let's begin. Amazon Rekognition is a service that enables you to quickly and easily integrate computer version features directly into your own applications. At its core, the rekogntion service 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 with an image to extracting labels or features from a video in a synchronize manner. Under the hood, Amazon Rekognition uses deep learning technology.

The service employs a convolution on your own network, CNN. Having been pre-trained on a massive amount of labeled ground truth data. Amazon takes ownership to build and train the underlying models. Which are both time consuming and competitionally expensive tasks. As such, the end result enables you to leverage the computer version features that Amazon Rekognition provides. Without having to in this time into building your own deep learning models. Nor having to consider or understand the complexities involved in operating a GPU enable cluster to build them, and the associated expense as well.

Amazon Rekognition is broadly separated into operation specific to processing images and operation specific for processing videos or video streams. The main difference between these two sets of APIs is that the image processing APIs follow a synchronize pattern, where has the video processing APIs follow an asynchronize pattern for calling. In this course, we'll cover both sets of APIs in separate lectures. Additionally, the rekognition API operations are grouped into those that are considered storage based and those that aren't. The storage based APIs support persisting certain facial features and service side containers known as collections. We'll cover off collections in a separate lecture.

But for now that concludes our lecture for the intro into the Amazon Rekognition Service. In the next lecture, we'll focus on the rekognition image processing APIs. Here we'll learn about the individual API operations and how we go about using them to perform image analysis. Go ahead and close this lecture and we'll see you shortly in the next one.