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The problem of predicting the training time of machine learning (ML) modelshas become extremely relevant in the scientific community. Being able topredict a priori the training time of an ML model would enable the automaticselection of the best model both in terms of energy efficiency and in terms ofperformance in the context of, for instance, MLOps architectures. In thispaper, we present the work we are conducting towards this direction. Inparticular, we present an extensive empirical study of the Full Parameter TimeComplexity (FPTC) approach by Zheng et al., which is, to the best of ourknowledge, the only approach formalizing the training time of ML models as afunction of both dataset's and model's parameters. We study the formulationsproposed for the Logistic Regression and Random Forest classifiers, and wehighlight the main strengths and weaknesses of the approach. Finally, weobserve how, from the conducted study, the prediction of training time isstrictly related to the context (i.e., the involved dataset) and how the FPTCapproach is not generalizable.

Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development