In this thesis we answer the following questions.Is machine learning a viable replacement for Ericsson’s CPU dimensioning tool CANDI? How do di↵erent learners perform on the problem at hand? How to fairly and accurately asses the performance of di↵erent learners?A framework for training, optimizing and evaluating Scikit-learn compatible estimators and a application for predicting EPG on SSR CPU usage are created.