Abstract: Methods and systems for improved optimization of machine learning model explanation processes are provided. In one embodiment, a method is provided that includes generating an approximation model using a model explanation process in order to present one or more relevant features for an output as generated by a model based on an input. Generating the approximation model may include determining a stability measure for a first set of parameter values for the model explanation process. The first set of parameter values to be iteratively adjusted to identify and optimize set of parameter values. The approximation model may then be generated using the model explanation process configured according to the optimize set of parameter values.

Abstract: Consumer threads can assist in performing progressive chunking for a data queue. For example, a consumer thread can determine a current-chunk identifier indicating a current memory chunk of an unbounded queue, where the current memory chunk is associated with a producer thread that is different from the consumer thread. The consumer thread can determine a target-chunk identifier indicating a target memory chunk to which the producer thread is to write a data item. In response to determining that the target-chunk identifier is greater than the current-chunk identifier, the consumer thread can append a new memory chunk to the unbounded queue for use as the target memory chunk by the producer thread.

Abstract: A method includes receiving input data associated with an application, the input data including at least one complex object and converting the at least one complex objects of the input data to a linearized set of features. The method further includes performing an explainability service on the application in view of the linearized set of features of the at least one complex object to generate an explanation array.

Abstract: A method includes receiving input data that is used by a machine learning model to generate a first inference. The machine learning model was previously trained using a set of training data. The method also includes generating a set of modified input data based on the input data. The set of modified input data is generated without using the training data. The method further includes obtaining, from an explanation system, an explanation array. The explanation system is to generate the explanation array based on the machine learning model and the set of modified input data.

Abstract: A method includes generating a rule unit as a containerized microservice on a cloud platform. The method further includes deploying, by a processing device, the containerized microservice on a container platform. The method further includes enabling message passing between the containerized microservice and additional containerized microservices over a shared channel.

Abstract: A method performed by a computing system includes, with a compiler, compiling source code to create an application for execution. The method further includes, after the compiling has started, recording a snapshot of compilation configurations, the compilation configurations including information obtained by the compiler during the compiling. The method further includes storing the snapshot in a predefined format and after storing the snapshot, loading the snapshot by configuring the compiler based on the compilation configuration in the snapshot.