A machine learning pipeline for optimizing bandwidth recommendations using offline policy learning with dual weighted ML agents.

Patentradars sammanfattning

The invention presents a software system combining historical data processing with dual machine learning agents to generate bandwidth recommendations through offline policy evaluation. Data flows through preparation, augmentation, and feature selection stages before training and testing phases. Two ML agents (First and Second) contribute weighted decisions to a recommendation engine that outputs optimized bandwidth allocations without requiring live policy deployment.

AI-genererad och redaktionellt processad.