Abstract: A system and method for generating product analytics insights for machine learning platforms is disclosed. The system and method can receive a log file including data derived while executing a software program. Software packages and the package sub-modules logged in the log file may be categorized into a plurality of package categories. A notebook in which the software code is written, and identified in the log file, may be categorized into a notebook category. A consumable insight may be generated based on: the categorized software packages and package sub-modules, and the categorized notebook. A natural language summary may be generated describing the consumable insight. The natural language summary may be transmitted for display on a graphical user interface (GUI) in response to a user query.

Abstract: This disclosure relates generally to a method and system for generative Al based unified virtual assistant. Conventional virtual assistant for enterprise systems needs to be configured for a specific industry or stakeholder and does not provide support for all stakeholders in the enterprise. Also, conventional rule-based virtual assistant or machine learning based virtual assistant need a large database for proper functioning. The disclosed method and system provide a unified virtual assistant for all processes in the enterprise. The unified virtual assistant provides support for all stakeholders in the enterprise and can answer all kinds of queries related to any process of the enterprise according to a role of a user logged into the system. The unified virtual assistant interprets user's query and generates effective prompts depending on the user's query which can be specific to customer, employee, executive or support desk users of the enterprise.

Abstract: Lead pooling and ranking includes implementing a pooling technique enhancing and optimizing lead scoring machine learning techniques for a set of leads. Lead pooling and ranking also includes generating a lead score for each configured rule, and combining one or more machine learning (ML) scores and one or more rules based scores to create a unitary score for each corresponding lead in the set of leads. Lead pooling and ranking further includes generating a rank and rating for each lead in the set of leads.

Abstract: Finding accurate prediction objectives includes building, by a framework application, a data pool for each of a plurality of prediction objectives. A plurality of machine learning (ML) models is trained for each data pool, and each of the plurality of ML models is combined for each data pool. One or more accurate objectives are identified and selected on the basis of a performance of the combined plurality of ML models.