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This project implements a scalable, cloud-based face recognition service using AWS infrastructure. It leverages several AWS services, including EC2 instances for processing, S3 for storing input and output images, and a custom auto-scaling algorithm for dynamic resource allocation based on demand

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Saketh1702/Face-Recognition-As-a-Service

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Face Recognition As a Service

Key Features:

  • AWS EC2 Instances: Handles the computational load for face recognition tasks.
  • S3 Buckets: Used to store both the input images and the processed results.
  • Custom Auto-Scaling Algorithm: Automatically scales the number of EC2 instances based on the number of requests in the SQS queue, ensuring cost-effective resource utilization.
  • SQS Queues: Manages request and response queues for image processing jobs.
  • Efficient Image Handling: The web tier uploads images to SQS queues, and the app tier processes them, sending back results through the response queue.

Technologies Used:

  • AWS EC2
  • AWS S3
  • AWS SQS
  • Python
  • Custom Auto-Scaling Logic

This repository demonstrates how to build a highly available and scalable face recognition service using cloud-based architecture.

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This project implements a scalable, cloud-based face recognition service using AWS infrastructure. It leverages several AWS services, including EC2 instances for processing, S3 for storing input and output images, and a custom auto-scaling algorithm for dynamic resource allocation based on demand

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