Abstract: A learning apparatus according to the present application includes: a dividing unit that divides predetermined learning data features of which are to be learned by a model by training, into a plurality of sets in chronological order; and a training unit that trains the model to learn the features of the learning data included in the set obtained by the division by the dividing unit, for each of the divided sets, in a predetermined order.

Abstract: An information processing method includes: an obtaining process of inputting each of plural sets of input data to be subjected to inference processing to a model and obtaining plural output value output by the model and representing inference results respectively corresponding to the plural set of input data, and reference information indicating a reference for evaluation of the model; and a processing process of selecting an evaluated data group to be used in the evaluation of the model, from the plural output values, by using a threshold determined on the basis of the reference indicated by the reference information obtained by the obtaining process and calculating an index value representing an evaluation value of the model by using the evaluated data group selected.

Abstract: A learning device according to the present application includes a generation unit that generates, from a plurality of values indicating features of a predetermined target and indicating different types of a plurality of features, a value corresponding to a set of the types of the features, and a learning unit that causes a model to learn a feature of the predetermined target using a value generated by the generation unit.

Abstract: A computer implemented method for generating and optimizing an artificial intelligence model, the method comprising receiving input data and labels, and performing data validation to generate a configuration file, and splitting the data to generate split data for training and evaluation; performing training and evaluation of the split data to determine an error level, and based on the error level, performing an action, wherein the action comprises at least one of modifying the configuration file and tuning the artificial intelligence model automatically; generating the artificial intelligence model based on the training, the evaluation and the tuning; and serving the model for production.