Online Inference

Online inference focuses on performing real-time predictions or estimations using models, particularly deep neural networks, as data streams arrive sequentially. Current research emphasizes improving efficiency and scalability through techniques like model compression (e.g., scattered online inference), adaptive algorithms (e.g., debiased SGD), and parallel processing (e.g., student parallelism in BERT-like models), often within resource-constrained environments. These advancements are crucial for deploying machine learning in applications requiring immediate responses, such as real-time control systems, online recommendation systems, and interactive AI systems, while addressing challenges related to computational cost and memory limitations.