The article discusses the challenges faced with a monolithic sentiment analysis system that struggled under high traffic. To overcome these limitations, the author details a pivot to microservices, enabling each component to scale independently. Key services such as API Gateway, text processing, and a GPU-based inference engine were isolated for efficiency. Containerization was a crucial step, utilizing Docker for setting up GPU-accelerated environments to enhance performance and achieve near real-time processing of social data.
By switching to micro-services, we isolated each function: API Gateway for routing requests, and dedicated services for text processing, inference, storage, and monitoring.
Containerizing our GPU-based inference service allowed us to utilize CUDA to enhance performance while scaling our sentiment analysis pipeline effectively.
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