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Turbocharging AI Sentiment Analysis: How We Hit 50K RPS with GPU Micro-services
- The article discusses how a sentiment analysis pipeline faced challenges due to high traffic, leading to the shift to microservices leveraging technologies like Kubernetes, GPU autoscaling, and a streaming ETL pipeline.
- Initially a monolithic design was used, but as traffic increased, over-provisioning every component became unsustainable, prompting the transition to microservices.
- Key components of the new microservices architecture include API Gateway, Text Cleanup & Tokenization, GPU-Based Sentiment Service, Data Storage & Logs, and Monitoring.
- Containerization was a pivotal step, with a Dockerfile for the GPU-enabled inference service enabling GPU acceleration and efficient orchestration via Kubernetes.
- Kubernetes allowed for GPU autoscaling based on utilization, ensuring optimal performance and cost efficiency by spinning up additional pods as needed.
- Batch inference and async I/O were utilized to boost performance, with multiple requests being batched together for efficient GPU resource utilization.
- Real-time ETL was achieved using Kafka for streaming, Spark for transformation, and Redshift for storage to handle high-volume social data ingestion.
- Challenges included initial memory issues and the importance of tuning batch sizes, balancing between analytics and end-user request latency.
- The integration of microservices, GPU acceleration, and a streaming-first ETL architecture led to a high-performance sentiment analysis pipeline capable of handling 50K RPS.
- The approach ensures minimal resource waste and scalability, allowing for adaptability to surging data volumes in real time.
- By embracing microservices and real-time data flows, the system can efficiently scale, ensuring reliability and readiness for any future traffic spikes.
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