Dispense and leak detection systems and methods

A method, comprising:

• receiving, via one of one or more computing devices, dispensing event data and leak data from a plurality of beverage dispensing devices over a network;
• training, via one of the one or more computing devices, a machine‑learning model using the dispensing event data and the leak data to detect dispensing events and leaks in beverage dispensing devices;
• receiving, via one of the one or more computing devices, time‑series pressure measurements from a first pressure sensor and a second pressure sensor of a particular beverage dispensing device;
• applying, via one of the one or more computing devices, the machine-learning model to the time-series pressure measurements to predict a leak in a fluid pathway of the particular beverage dispensing device;
• and
• in response to predicting the leak in the fluid pathway, performing, via one of the one or more computing devices, a remedial action.

A non-transitory computer-readable medium embodying a program that, when executed by at least one computing device, causes the at least one computing device to:

• receive dispensing event data and leak data from a plurality of beverage dispensing devices over a network;
• train a machine‑learning model using the dispensing event data and the leak data to detect dispensing events and leaks in beverage dispensing devices;
• receive time‑series pressure measurements from a first pressure sensor and a second pressure sensor of a particular beverage dispensing device;
• apply the machine-learning model to the time-series pressure measurements to predict a leak in a fluid pathway of the particular beverage dispensing device;
• and
• in response to predicting the leak in the fluid pathway, perform a remedial action.