Designing Distributed Systems for On-Demand Scoring and Prediction Services
Abstract
The research focuses on the key design principles required for implementing a distributed system that efficiently handles high-throughput prediction requests while ensuring minimal latency and high availability. The proposed architecture utilizes a modular microservices framework to enable independent scaling, seamless deployment, and dynamic load management. Each microservice is responsible for a specific function, such as data ingestion, feature transformation, model serving, and request routing, allowing for high cohesion and low coupling in the system design. This approach enables teams to independently update and maintain individual components without disrupting the overall service.