Abstract: A method and a system for generating required feature output by providing a FaaS (Function-as-a-Service) based feature library using a DAG (Directed Acyclic Graph), are disclosed. A library is configured to store logic codes being generated for a plurality of features and the library is then deployed on a FaaS engine. At least one feature selection configuration from a user is received by the FaaS engine. The DAG implements at least one transformation based on the at least one feature selection configuration, and the FaaS engine thereby generates the required feature output for the user.

Abstract: A method and a system for refining column mappings using byte level attention based neural model are disclosed. Based on a plurality of synthetic data and a plurality of input column names, an encoded data is generated for each of the one or more by present in the input column names. The encoded data is used to train a deep learning (DL) model having a word level auto regressive decoder for identifying at least one meaning for each byte of each of the received plurality of input column names. Further, a plurality of pre-existing column descriptions may be used to determine whether or not the identified at-least one meaning matches with at least one description of the plurality of column descriptions. Subsequently, fine tuning or refining of the meanings may be conducted to adequately obtain corresponding mapping prediction output.