Real Time and Online Learning Systems Questions

Designing machine learning systems that learn, adapt, and act in real time on streaming data. Topics include online learning algorithms (online gradient descent, incremental learners, contextual bandits), handling concept drift, model freshness versus computational cost trade offs, low latency model updates and serving, streaming feature engineering and feature stores, feedback loops, evaluation strategies for online learners, exploration versus exploitation, data labeling and delayed feedback, reliability and monitoring of models in production, and integration with streaming processing frameworks for both inference and continuous training.