Abstract: A method may include obtaining a set of preliminary tabular datasets and tasks to be performed by preliminary machine-learning (ML) pipelines. The method may further include training a meta-model that predicts performance of ML pipelines in performing the tasks using the preliminary ML pipelines, the preliminary ML pipelines synthesized as different approaches for performing the tasks. The method may also include obtaining a candidate tabular dataset and predicting, using the meta-model, performance of a plurality of candidate ML pipelines for performing the tasks on the candidate tabular dataset. The method may also include selecting a threshold number of top-performing candidates of the plurality of candidate ML pipelines as predicted by the meta-model for training to perform the tasks. In addition, the method may include identifying a top-performing ML pipeline based on performance of the trained top-performing candidates.

Abstract: According to an aspect of an embodiment, operations include receiving data comprising tabular datasets and code files. The operations further include generating a task specification corresponding to each dataset and determining data type information for features of each dataset. The operations further include extracting a plurality of API methods from the code files and generating an ML pipeline based on the data type information and the task specification. The operations further include obtaining variations of the ML pipeline based on options associated with at least one ML component and generating a database of pipelines based on the ML pipeline and the variations. The operations further include selecting candidate ML pipelines from the database based on an optimization approach and executing the candidate ML pipelines to evaluate a performance of each candidate pipeline on test data. The operations further include obtaining a training corpus of ML pipelines for pipeline recommendation.

Abstract: A storage medium storing a program that causes one computer to execute a process includes, inputting voice information that indicates a conversation voice to an identification model generated by using learning data associated with two groups of persons, that identifies a speaker, to identify a speaker who has spoken in speech section included in the conversation voice; classifying the speech section based on a voice characteristic of the speech section; and outputting a result of classifying the speech section in which the speaker is identified as the different person.