A Comparative Evaluation of Adaptive Machine Learning Models for Low-Latency Enterprise Data Processing

Abstract

With the rising requirements for enterprise data processing, intelligent models are becoming a must-have to be able to deliver accurate predictions under tight latency, throughput and resource requirements. Yet traditional machine learning systems tend to be based on set model architectures that can become suboptimal under varying data distributions, workloads, and computational resources. The authors introduce a framework for comparing adaptive machine learning models with low-latency enterprise data processing capabilities. The framework takes into account incremental learning, adaptive decision trees, online ensemble learning, lightweight neural networks and prediction latency, throughput, adaptability, computational overhead and predictive accuracy. A common experimental approach is established to test these models under variable data arrival rates, concept drift and heterogeneous processing requirements. The metrics used to assess performance include latency percentiles, throughput, classification accuracy, adaptation delay and normalized resource utilization. Analytical formulations are introduced to help with the multi-dimensional comparison and selection of models for deployment. However, as empirical measurements are not given, the numerical comparison is not meant to be an experimental confirmation but rather an illustration. The framework generated as a result allows for a reproducible model for selecting adaptive learning for enterprise applications with latency sensitivity.