Abstract: According to an aspect of an embodiment, operations may include receiving an ML project stored in an ML corpus database. The operations may further include mutating a first ML pipeline, of a set of first ML pipelines associated with the received ML project, to determine a set of second ML pipelines. The mutation of the first ML pipeline may correspond to a substitution of a first ML model associated with the first ML pipeline with a second ML model associated with one of the set of predefined ML pipelines. The operations may further include selecting one or more ML pipelines from the set of second ML pipelines based on a performance score associated with each of the determined set of ML pipelines. The operations may further include augmenting the ML corpus database to include the selected one or more ML pipelines and the set of first ML pipeline.

Abstract: According to an aspect of an embodiment, operations may include receiving an ML project stored in an ML corpus database. The operations may further include mutating a first ML pipeline, of a set of first ML pipelines associated with the received ML project, to determine a set of second ML pipelines. The mutation of the first ML pipeline may correspond to a substitution of a first ML model associated with the first ML pipeline with a second ML model associated with one of the set of predefined ML pipelines. The operations may further include selecting one or more ML pipelines from the set of second ML pipelines based on a performance score associated with each of the determined set of ML pipelines. The operations may further include augmenting the ML corpus database to include the selected one or more ML pipelines and the set of first ML pipeline.

Abstract: According to an aspect of an embodiment, operations may include receiving an ML project including a data-frame and an ML pipeline including a plurality of code statements associated with a plurality of features corresponding to the ML project. The operations may further include determining one or more atomic steps corresponding to the ML pipeline to determine an atomized ML pipeline. The operations may further include instrumenting the atomized ML pipeline to determine an instrumented ML pipeline including one or more operations corresponding to the ML project. The operations may further include executing the instrumented ML pipeline to capture one or more data-frame snapshots based on each of the one or more operations. The operations may further include constructing a feature provenance graph (FPG). The operations may further include identifying one or more discarded features, from the plurality of features corresponding to the ML project, based on the constructed FPG.

Abstract: According to an aspect of an embodiment, operations may include selecting, from a training dataset, a first data point as a seed data point. The operations may further include generating a population of data points by application of a genetic model on the seed data point. The population of data points may include the seed data point and a plurality of transformed data points of the seed data point. The operations may further include determining a best-fit data point in the generated population of data points based on application of a fitness function on the generated population of data points. The operations may further include executing a training operation on the DNN based on the determined best-fit data point. The operations may further include obtaining a trained DNN for the first data point based on the training operation on the DNN based on the determined best-fit data point.

Abstract: Operations include obtaining a machine learning (ML) pipeline skeleton that indicates a set of first functional blocks to use to process a new dataset of a new ML project. Additionally, for each respective first functional block of the set of first functional blocks, the operations include obtaining existing code snippets from existing ML pipelines, each of the existing code snippets instantiating a second functional block of the existing ML pipelines and being a potential instantiation of the respective first functional block. The operations also include determining a respective adaptability, with respect to the new dataset, of each of the existing code snippets and selecting a particular existing code snippet for implementation of the respective first functional block based on the determined adaptabilities. Further, the operations include instantiating the pipeline skeleton based on the particular existing code snippets.

Abstract: Operations include obtaining a machine learning (ML) pipeline skeleton that indicates a set of first functional blocks to use to process a new dataset of a new ML project. The operations also include obtaining a relationship mapping that maps dataset features to respective functional blocks, the relationship mapping indicating correspondences between dataset features of existing datasets of existing ML projects and usage of second functional blocks of existing ML pipelines of the existing ML projects. The operations also include mapping the first functional blocks to respective portions of the new dataset based on the relationship mapping. In addition, the operations include instantiating the pipeline skeleton with respective code snippets that each correspond to a respective first functional block of the set of first functional blocks, the respective code snippets each including one or more respective code elements that are based on the mapping of the first functional blocks.

Abstract: According to an aspect of an embodiment, operations may include selecting, from a training dataset, a first data point as a seed data point. The operations may further include generating a population of data points by application of a genetic model on the seed data point. The population of data points may include the seed data point and a plurality of transformed data points of the seed data point. The operations may further include determining a best-fit data point in the generated population of data points based on application of a fitness function on the generated population of data points. The operations may further include executing a training operation on the DNN based on the determined best-fit data point. The operations may further include obtaining a trained DNN for the first data point based on the training operation on the DNN based on the determined best-fit data point.