Amazon Rekognition is a powerful, fully managed computer vision service that enables easy integration of image and video analysis into your applications. With Rekognition, you can automatically detect objects, people, text, scenes, and activities using machine learning models without needing to build or train your own. When paired with services like Amazon S3, Amazon SQS, and AWS Lambda, you can create a scalable, event-driven image-processing pipeline with minimal operational overhead.
In this Cloud Lab, you will build an automated image labeling pipeline using Amazon Rekognition and key AWS serverless services. You’ll begin by creating a DynamoDB table and an Amazon S3 bucket. The bucket will act as your image store. Then, you’ll set up an Amazon SQS queue to capture notifications whenever new images are uploaded. Next, you’ll create an AWS Lambda function that is triggered through the queue, processes incoming image messages, and uses Amazon Rekognition to detect labels within each image. The Lambda function will then store the extracted labels and related metadata in an Amazon DynamoDB table for fast and scalable retrieval.
Below is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
