CNC End effector tools wear SOH prediction: a big-data approach Pertaining to the high cost of maintenance of various machines in the industrial sector, there is a growing need of efficient and cost saving techniques to predict the state of health(or remaining useful life of a machine).It is obvious that applying efficient techniques can lead to a competitive advantage in the industrial sector. Our thesis extends the work done previously on the MorePRO project based on the estimation of the state of health of CNC machines. The contrast with the previous work is to focus on cloud based computing rather then previously performed analysis with edge computing which is related to the data collected in the local environment from the machine.