Requirements driven machine learning models for technical configuration

Abstract

Techniques and solutions are provided for obtaining a suggested configuration for a configurable object. Typically, a particular object and object configuration are recommended based on technical characteristics of the object. However, a user or process wishing to obtain a recommendation may be more familiar with their operational requirements. Disclosed techniques can include an overall solutions category containing solutions of different solutions category subtypes. Sets of requirements attributes and configuration (technical) attributes can be defined for the solutions category. In some cases, a first machine learning model is trained using input values for the requirements attributes and the configuration attributes, and is used to recommend a particular solution in response to a set of input requirement attribute values. Different machine learning models can be trained for the various solutions, including using the configuration attributes for a particular solution, and can be used to recommend a configuration of a selected/recommend solution.

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