Computerized method for determining a compression rate (r*) for an AI model of an industrial task (Task 1, ..., k; Task k+1, ..., k+n) according to a set of requirements for the runtime properties of the AI model, wherein, for the AI model, the original number of parameters (n) is reduced to a reduced number of parameters (n*), wherein in a first stage for a number of different AI models for a number of industrial tasks (Task 1, ..., k) for each of the industrial tasks (Task 1, ..., k) a number of different compression rates (r i) for the assigned AI model is determined, wherein in a first step each AI model is compressed with that number of different compression rates (r i ) wherein in a second step each of the compressed AI models is executed in an execution environment, whereby as first results during the execution of each of the AI models the runtime properties are recorded, and wherein in a third step an optimal compression rate r* for each of the AI models is calculated by an analysis of the first results, whereby the optimal compression rate (r*) for each industrial task (Task 1, ..., k) together with a description of the industrial task is stored in a database, characterized in, that in a second stage the data from the database is used to train an additional machine learning model, the additional machine learning model having feature information about each of the industrial tasks (Task 1, ..., k) as input and the calculated optimal compression rate (r*) as output, the feature information at least comprising information of memory allocation limit, inference time limit for the compressed model, an original AI model size of the uncompressed AI model, and used compression algorithm, and that in a third stage for a new AI model of a new task (Task k+1, ..., k+n) a new set of desired runtime properties is defined and the additional machine learning model is employed for determining the optimal compression rate (r*) for that new AI model in respect to the desired runtime properties. wherein, for the AI model, the original number of parameters (n) is reduced to a reduced number of parameters (n*), wherein in a first stage for a number of different AI models for a number of industrial tasks (Task 1, ..., k) for each of the industrial tasks (Task 1, ..., k) a number of different compression rates (r i) for the assigned AI model is determined, wherein in a first step each AI model is compressed with that number of different compression rates (r i ) wherein in a second step each of the compressed AI models is executed in an execution environment, whereby as first results during the execution of each of the AI models the runtime properties are recorded, and wherein in a third step an optimal compression rate r* for each of the AI models is calculated by an analysis of the first results, whereby the optimal compression rate (r*) for each industrial task (Task 1, ..., k) together with a description of the industrial task is stored in a database, characterized in, that in a second stage the data from the database is used to train an additional machine learning model, the additional machine learning model having feature information about each of the industrial tasks (Task 1, ..., k) as input and the calculated optimal compression rate (r*) as output, the feature information at least comprising information of memory allocation limit, inference time limit for the compressed model, an original AI model size of the uncompressed AI model, and used compression algorithm, and that in a third stage for a new AI model of a new task (Task k+1, ..., k+n) a new set of desired runtime properties is defined and the additional machine learning model is employed for determining the optimal compression rate (r*) for that new AI model in respect to the desired runtime properties. <EOS>