Batch Data

Batch data processing focuses on efficiently handling and analyzing large datasets processed in groups, aiming to improve computational speed and resource utilization while maintaining accuracy. Current research emphasizes optimizing batching techniques for various applications, including large language model inference (using algorithms like DFTSP and SkipDecode), Bayesian optimization (with scalable acquisition functions), and dynamic deep learning (employing finite state machines and compiler optimizations). These advancements are significant for improving the efficiency and scalability of machine learning models across diverse fields, from manufacturing process optimization to personalized medicine.