Abstract: The present application relates to developing and deploying machine learning analysis systems using a low-code or no-code approach. A cloud service is configured to receive a first data stream from a sensor device and train a machine-learning model to recognize selected elements of the first data stream that are selected from a package of template models via a graphical user interface. The cloud service deploys the machine-learning model to an edge datacenter configured to receive a second data stream via a network connection. The edge datacenter locally interrogates the second data stream based on the machine-learning model to generate an element set including the selected elements. A logic service may receive a selection of one or more properties of the element set and one or more logical operators via a graphical user interface to generate user-configured logical rules. The logic service may the user-configured logical rules to the element set.

Abstract: The present application relates to developing and deploying machine learning analysis systems using a low-code or no-code approach. A cloud service is configured to receive a first data stream from a sensor device and train a machine-learning model to recognize selected elements of the first data stream that are selected from a package of template models via a graphical user interface. The cloud service deploys the machine-learning model to an edge datacenter configured to receive a second data stream via a network connection. The edge datacenter locally interrogates the second data stream based on the machine-learning model to generate an element set including the selected elements. A logic service may receive a selection of one or more properties of the element set and one or more logical operators via a graphical user interface to generate user-configured logical rules. The logic service may the user-configured logical rules to the element set.