Backend

Features

• Serverless Deployment with AWS Lambda
• Continuous, Efficient Deployment with AWS SageMaker: Ensure streamlined, fine-tuned deployments of the YoloV8 model.
• Real-Time Frontend-Backend Communication with WebSockets: Maintain a live, persistent connection to poll for results.
• Concurrent User Request Handling with AWS SQS: Manage and process multiple user requests simultaneously.

Important Files

• lambda-container: Directory with Dockerfile to build Docker container that is deployed to AWS Lambda and runs entire ML and backend pipeline, after being triggered request from frontend.
• lambda-container/lambda_function.py: Runs the entire ML pipeline, and communicates with AWS components. Deployed via Docker container to our main AWS Lambda function.