CPC G06N 20/00 (2019.01) [G06F 16/9024 (2019.01); G06F 21/6209 (2013.01); G06N 5/022 (2013.01); G06N 5/04 (2013.01)] | 18 Claims |
1. A computer-implemented method comprising:
responsive to training each model of a plurality of models using a software platform:
receiving, from a user associated with the model, client data, a use-case description comprising a user-input natural text description of an intended use-case of the model including an expected type of output from the model, and a selection of local data sources to be used in the model, wherein the local data sources are accessible by the software platform;
generating, based on the client data, a client data profile;
determining, for each feature of a plurality of features associated with the selected local data sources, a feature importance, wherein the feature importance indicates an impact that feature had on each of the plurality of models;
generating, based on the use-case description, a use-case profile comprising a vector embedding encoding the respective user-input natural text description;
generating, based on a plurality of determined client data profiles, a plurality of determined feature importances associated with features associated with local data sources, and a plurality of determined use-case profiles, a feature profile relation graph comprising a plurality of client data profile nodes, a plurality of local feature nodes and a plurality of use-case profile nodes, wherein each local feature node of the plurality of local feature nodes is associated with one or more client data profile nodes and one or more user-case profile nodes by a respective edge having an associated edge weight, wherein a given edge is assigned an edge weight that represents a strength of relationship between the respective local feature node and one of the client data profile nodes and the user-case profile nodes;
responsive to receiving a new client data set and a new use-case description comprising a new user-input natural text description of a new intended use-case, determining, based on the new client data set, the new use-case description and the feature profile relation graph, one or more local features as suggested local features for use in building a new model; and
automatically initiating training of the new model based on the new client data set and the suggested local features.
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