Abstract: The present disclosure relates to a computer-implemented method for automated determination of a model compression technique for compression of an artificial intelligence-based model, a corresponding computer program product, and a corresponding apparatus of an industrial automation environment. The method includes automated provisioning of a set of model compression techniques using an expert rule, determining metrics for the model compression techniques of the set of model compression techniques based on weighted constraints, and selecting an optimized model compression technique based on the determined metrics.

Abstract: System, Device and Method of detecting at least one abnormal datapoint in operation data (U) associated with an industrial environment (610) is disclosed. The method comprising iteratively applying one or more anomaly detection models (fi) to at least one subset (S) of the operation data (U), wherein the anomaly detection models (fi) are trained based on a training dataset (L) consisting of datapoints labeled as normal; classifying subset-datapoints in the subset (S) as one of normal datapoints (N) and abnormal datapoints (A) using the anomaly detection models (fi); updating the training dataset at least with the normal datapoints; retraining the anomaly detection models (fi) with the updated training dataset after expiration of a threshold time, wherein the threshold time is based on the number of updates to the training dataset; and detecting the at least one abnormal datapoint in the operation data (U) using the anomaly detection models (f?i).

Abstract: A recommendation system and method for determining a compression rate for an AI model of an industrial task, wherein the parameters are reduced to a reduced number of parameters for the AI model, where each AI model is compressed with different compression rates in a first stage, where each compressed AI model is executed and the runtime properties are recorded as first results during the executions and an optimal compression rate is calculated by analyzing the first results and stored in a database, wherein data from the database is used to train an additional machine learning model in a second stage and, in a third stage, for a new AI model of a new task, a new set of desired runtime properties is defined and the additional model is employed for determining the optimal compression rate for that new AI model with respect to the desired runtime properties.