How do you design and implement a real-time machine learning system?

Real-time ML systems require careful architecture design for low latency, high throughput, and reliable predictions.\n\n• Architecture: Streaming data pipelines, model serving infrastructure, caching layers\n• Technologies: Kafka for streaming, Redis for caching, Docker for deployment\n• Model optimization: Feature preprocessing, model compression, batch prediction\n• Monitoring: Latency metrics, prediction accuracy, system health dashboards\n\nExample: Fraud detection system processes transactions in <100ms using Kafka streams, cached feature store, optimized XGBoost model, and real-time monitoring. Implements fallback mechanisms and gradual model updates without service interruption.