Abstract: In a first aspect of the invention, there is a computer-implemented method including: training, by a computing device, a machine learning (ML) model on a financial data set comprising data on financial behavior applicable to repaying debts; entering, by a computing device, an interaction with a user regarding renegotiating a debt; and generating, by the computing device, a proposed payment plan for the debt, based on user data of the user, and using the ML model trained on the financial data set.

Abstract: A computer-implemented method is described that provides an initial workload sizing to be run on a cluster. This includes receiving a configuration file defined to create a container using verified cluster limits. The configuration file is verified and sections of interest containing input values to define a family of a container definition are extracted. The verified configuration file is classified as Good, Bad or Neutral by a trained classification model using the input values. The configuration file is tagged with the classification, and Neutral classifications are tagged as Good with a warning. For a Bad classification, a knowledge database is consulted to identify whether family specification limits exist. If the family limits exist, the configuration file is adjusted using a new set of cluster limits associated with the family specification. If the family limits do not exist, the configuration file is adjusted using a new set of aleatory updates.