Implement Serverless Machine Learning Inference with AWS Lambda

Abstract: Serverless computing has emerged as a transformative paradigm for deploying machine learning models at scale without infrastructure management overhead. Traditional deployment approaches require dedicated servers with fixed capacity and 24/7 operational costs, creating barriers for machine learning adoption in cost-sensitive applications. This paper presents the implementation of a serverless machine learning inference system using AWS Lambda, demonstrating its application in intelligent loan approval decisions. The system implements a complete end-to-end architecture combining modern web technologies (React.js, FastAPI, MongoDB) with AWS serverless services (Lambda, API Gateway, DynamoDB) to deliver real-time ML inference with automatic scaling and pay-per-use economics. The core contribution is a transparent AI risk evaluation engine that analyzes five financial parameters— Debt Service Ratio, Loan-to-Income Ratio, credit score, employment stability, and dependent count—to compute composite risk scores (0- 100) and generate instant loan decisions (APPROVED, CONDITIONAL, REJECTED) with complete explainability. The serverless architecture eliminates infrastructure provisioning, enables automatic scaling from zero to thousands of concurrent requests, and reduces operational costs by 70% compared to traditional always-on server deployments. Performance evaluation demonstrates average inference response times under 3 seconds, System Usability Scale score of 83.5 (Excellent), and zero security vulnerabilities across comprehensive penetration testing. The system processes loan applications 80x faster than traditional manual processes while maintaining complete transparency and eliminating human bias through deterministic algorithms. This implementation validates serverless computing as a viable, cost- effective architecture for deploying production-grade machine learning inference systems in financial services and demonstrates practical patterns for Lambda function development, API Gateway integration, and DynamoDB optimization.

Keywords: Serverless Computing, AWS Lambda, Machine Learning Inference, Real-time Prediction, API Gateway, DynamoDB, FastAPI, React, Cloud Architecture, Scalable ML Deployment.