🎥 👁️ Getting started with Amazon Rekognition using AWS Lambda
🎥 👁️ AWS REKOGNITION LAB | Computer Vision with Lambda and S3 How can we automatically analyze an image without having to train our own Machine Learning model? In this new Deploy on Friday hands-on lab, we'll build a serverless architecture using: Amazon S3 → AWS Lambda → Amazon Rekognition During the lab, we'll learn how to: ✅ Create an Amazon S3 bucket and upload images. ✅ Create and…
In this hands-on lab, we explore how to automatically analyze images without the need to train our own machine learning model. By building a serverless architecture using Amazon S3, AWS Lambda, and Amazon Rekognition, we can learn the process step by step.
First, we create an Amazon S3 bucket and upload images. Then, we create and configure an IAM Role. Next, we deploy an AWS Lambda function. After that, we read the images stored in Amazon S3 from Lambda. We then integrate Lambda with Amazon Rekognition. This allows us to detect objects, scenes, and elements inside an image.
We analyze the response returned by Rekognition to understand the results. Throughout this process, we gain knowledge about event-driven image processing on AWS. Amazon Rekognition offers various computer vision capabilities such as object detection, text recognition, facial analysis, content moderation, and more.
The architecture of this lab involves S3, Lambda, Rekognition, and the response. It demonstrates how different AWS services can work together to build real-world solutions. This lab is another practical example showing how various AWS services can collaborate to create useful applications. Deploy on Friday, what could possibly go wrong?
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