Abstract: Determining cognitive models to be deployed at auxiliary devices may include maintaining relations, e.g., in a database. The relations map hardware characteristics of auxiliary devices and example datasets to cognitive models. Cognitive models are determined for auxiliary devices, based on said relations, e.g., for each of the auxiliary devices. An input dataset is accessed, which comprises data of interest, e.g., collected at a core computing system (CCS), and hardware characteristics of each of the auxiliary devices. An auxiliary cognitive model is determined based on a core cognitive model run on the input dataset accessed, wherein the core cognitive model has been trained to learn at least part of said relations. Parameters of the auxiliary model determined can be communicated to said each of the auxiliary devices for the latter to deploy the auxiliary model determined. Method may be implemented in a network having an edge computing architecture.

Abstract: Determining cognitive models to be deployed at auxiliary devices may include maintaining relations, e.g., in a database. The relations map hardware characteristics of auxiliary devices and example datasets to cognitive models. Cognitive models are determined for auxiliary devices, based on said relations, e.g., for each of the auxiliary devices. An input dataset is accessed, which comprises data of interest, e.g., collected at a core computing system (CCS), and hardware characteristics of each of the auxiliary devices. An auxiliary cognitive model is determined based on a core cognitive model run on the input dataset accessed, wherein the core cognitive model has been trained to learn at least part of said relations. Parameters of the auxiliary model determined can be communicated to said each of the auxiliary devices for the latter to deploy the auxiliary model determined. Method may be implemented in a network having an edge computing architecture.

Abstract: A machine-learning model generation method, system, and computer program product deciding, via a first algorithm, a machine-learning algorithm that is best for customer data, invoking the machine-learning algorithm to train a neural network model with the customer data, analyzing the neural network model produced by the training for an accuracy, and improving the accuracy by iteratively repeating the training of the neural network model until a customer-defined constraint is met, as determined by the first algorithm.