A computer-implemented method, carried out by one or more computers, for creating computerized numerical control data sets for controlling machine tools in a usage environment, wherein the control data sets are read in from associated machine tools for machining starting materials, in particular for machining metal or sheet-metal parts using cutting, shaping and/or joining methods, the method comprising the steps: - receiving (step 201) a first component data set representing a digital design model of a first component; - creating (step 203) a first computerized numerical control data set for the first component data set using a control program generation software, wherein the control program generation software includes an assessment routine which uses a trained machine learning algorithm with settable parameters (Pi), wherein starting values of the settable parameters (Pi) were determined by training a machine learning training algorithm which corresponds to the trained machine learning algorithm; - compiling (step 205) a first additional training data set from the first component data set and the created computerized numerical control data set, and outputting (step 207) the first additional training data set to a usage-environment-specific training database; - updating (step 209) the machine learning algorithm by setting usage-environment-specific values for the settable parameters (Pi), wherein the usage-environment-specific values were determined by training the machine learning training algorithm using the usage-environment-specific training database; - receiving (step 211) a second component data set representing a digital design model of a second component; and - creating (step 213) a second computerized numerical control data set for the second component data set using the control program generation software and running the assessment routine, wherein the machine learning algorithm used is the one which is updated with respect to the settable parameters (Pi). - receiving (step 201) a first component data set representing a digital design model of a first component; - creating (step 203) a first computerized numerical control data set for the first component data set using a control program generation software, wherein the control program generation software includes an assessment routine which uses a trained machine learning algorithm with settable parameters (Pi), wherein starting values of the settable parameters (Pi) were determined by training a machine learning training algorithm which corresponds to the trained machine learning algorithm; - compiling (step 205) a first additional training data set from the first component data set and the created computerized numerical control data set, and outputting (step 207) the first additional training data set to a usage-environment-specific training database; - updating (step 209) the machine learning algorithm by setting usage-environment-specific values for the settable parameters (Pi), wherein the usage-environment-specific values were determined by training the machine learning training algorithm using the usage-environment-specific training database; - receiving (step 211) a second component data set representing a digital design model of a second component; and - creating (step 213) a second computerized numerical control data set for the second component data set using the control program generation software and running the assessment routine, wherein the machine learning algorithm used is the one which is updated with respect to the settable parameters (Pi).