Abstract: A system includes a memory and a processing device, operatively coupled to the memory, to receive, from a client device via a user interface, input data comprising an initial version of a machine learning model, initialize an operating mode of the user interface for machine learning model building, and generate an enhanced version of the machine learning model in accordance with the operating mode.

Abstract: Implementations disclosed include methods and systems that include obtaining, by a processing device, a first output from a machine learning model, wherein the first output comprises one or more data samples; identifying a first set of data samples of the one or more data samples that satisfies a threshold criterion; generating, using an explainability tool, a weighted value for each data sample of the first set of data samples; and modifying the machine learning model based at least in part on the weighted value for each data sample of the first set of data samples.

Abstract: A system includes memory and a processing device, operatively coupled to the memory, to obtain an input signal corresponding to data obtained from a data source, extract a set of features using the input signal, generate a set of feature tracking data from the set of features, compress a machine learning model to obtain a compressed model by identifying a subset of features based on the set of tracking data, and use the compressed model to make a prediction based on the set of feature tracking data. The set of features includes a set of confidence features and a set of uncertainty features, and the set of feature tracking data includes a set of confidence feature tracking data and a set of uncertainty feature tracking data.