01470nas a2200145   4500000000100000000000100001008004100002260004800043100002000091700002300111245007400134856004900208520104500257020002201302       2025                            d  c05/2025bSpringer NatureaKonstanz, Germany1 aMateusz Żarski1 aSławomir Nowaczyk00aBalancing Performance and Scalability of Demand Forecasting ML Models  uhttps://doi.org/10.1007/978-3-031-91398-3_103 a<p>Balancing performance and scalability is a major concern when developing robust ML models for diverse, big-data scenarios, such as predicting demand for a number of products across multiple locations. The two mutually opposite approaches are to use a single ML model for maximizing scalability, often at the expense of performance, or to use a specialized model for each specific use case, which is often prohibitive in terms of computational costs. In this paper, we propose to balance those two approaches using our methods of model clustering and grouping. We achieve the performance level of a single use-case model while preserving the global scalability of the solution. In our experiments, we use a publicly available demand forecasting dataset as a use case. We develop and train baseline shallow ML models and DL models for both maximizing performance and scalability. Then, we showcase a desirable balance that can be achieved using our proposed methods, one that outperforms both shallow ML and specific use-case models.</p>
  a978-3-031-91398-3