Abstract: Systems and methods of the present disclosure relate to a machine learning model-based rules generator for generating rules for building management systems. A method can include receiving, by one or more processors, a prompt comprising natural language data regarding a rule associated with operation of an item of equipment of a building; providing, by the one or more processors, the prompt as input to a machine learning model to cause the machine learning model to generate a representation of the rule; and activating, by the one or more processors, the rule for the item of equipment.

Abstract: Systems and methods described herein are directed to the optimization and implementation of machine-learning models on edge devices. A building management system can receive optimization criteria to optimize a first machine-learning model for a target platform. The first machine-learning model has one or more model parameters. The building management system transforms, based on the target platform and the optimization criteria, at least one datatype of the one or more model parameters of the first machine-learning model to generate a second machine-learning model. The building management system determines, using a verification dataset, an accuracy of the second machine-learning model, and retrains the second machine-learning model using a training dataset responsive to the accuracy being less than a predetermined threshold.

Abstract: A set of processes enable supervised learning of a machine learning model without human intervention by producing the positive and negative examples at-will in a deployed environment. A technique implements a series of events that replaces the need for human intervention to generate labeled data for supervised learning. This enables automatic retraining of the model in a deployed environment without the need for human labeled data, supporting audio and video data.