INOUT OPTIMA: Trading Off Machine Learning Prediction Quality with Data Quantity for Network Optimization
Marco Reisacher, Nikolaos Mitsakis, Andreas Blenk · 2025
The importance of machine learning (ML) in factories and plants is growing; however, network operators have not yet fully explored the optimization potential that ML applications offer. The literature lacks a detailed analysis of the trade-offs between data quantity and model accuracy in the context of communication demands. This work introduces INOUT OPTIMA, a benchmarking framework for industrial ML applications. INOUT OPTIMA highlights the extensive optimization opportunities ML provides for network planning by analyzing the relationship between data quantity and accuracy. Specifically, it evaluates ML models trained on datasets of varying quality, subjected to controlled data degradation scenarios. This approach allows us to assess how training on degraded data affects inference accuracy and gain knowledge on the behavior of ML applications under degraded input data. The results underline the importance of understanding these trade-offs for factory operators, enabling the design of resilient and efficient ML applications, and providing additional data for factory planners.