PL1841I - Strengthening Enterprise Systems Machine Learning Models for Black Swan

Enterprise systems are increasingly relying on Machine Learning (ML) models to optimize decision-making and enhance operational efficiency across business functions. The effectiveness and reliability of these models are contingent upon their ability to generalize unseen data and adapt to evolving business scenarios. This talk delves into the benefits of testing ML models with out-of-distribution (OOD) data in the context of enterprise systems, emphasizing the importance of evaluating model performance and robustness under diverse conditions. A good example here could be making supply chain demand forecasting models better prepared for real-world situations where unforeseen events or changing market conditions might affect the data distribution.