Intelligent edge computing platform with machine learning capability

A method comprising:

• providing an edge platform connected to a local network between a plurality of sensors and a remote network;
• using an edge converter to create an edge-converted machine learning model from a non-edge-based machine learning model that is trained and operable in non-real-time on aggregated static sensor data, wherein the edge-converted machine learning model comprises analytics expressions of an analytics expression language that are operable to process streams of data in real-time;
• providing on the edge platform the edge-converted machine learning model that is optimized to operate with the computing resources of the edge platform in real-time on one or more streams of sensor data and produce one or more streams of inferences;
• receiving a first sensor data stream from a first sensor of the plurality of sensors at the edge platform;
• without first transmitting the first sensor data stream to the remote network for processing:
• executing the edge-converted machine learning model using the computing resources of the edge platform to operate on the first sensor data stream in real-time and produce a stream of first inferences about a first network device of the plurality of network devices;
• and
• determining based on the stream of first inferences whether to take an action regarding the first network device without first communicating the inferences to the remote network.

A method comprising:

• providing on an edge platform of a distributed network an edge-converted machine learning model adapted to operate on a continuous stream of data in real-time and to produce a continuous stream of inferences in real-time;
• receiving at the edge platform a first sensor data stream from a first sensor;
• at the edge platform without first transmitting the first sensor data stream to the remote network for processing:
• operating on the first sensor data stream with the edge-converted machine learning model and producing a stream of first inferences about a first network device;
• and
• determining based on the stream of first inferences whether to take an action affecting the first network device;
• updating the edge-converted machine learning model on the edge platform without interrupting the operating on the first sensor data stream or the producing the stream of first inferences, wherein the updating comprises:
• generating a continuous model stream representing the edge-converted machine learning model;
• streaming the model stream along with the first sensor data stream on the edge platform;
• providing on the edge platform a modified edge-converted machine learning model;
• inserting the modified edge-converted machine learning model into the model stream;
• detecting a change in the model stream corresponding to the modified edge-converted machine learning model as the modified machine learning model propagates in the model stream;
• in response to detecting the change in the model stream instantiating an instance of the modified edge-converted machine learning model on the edge platform;
• and
• going forward using the instantiated instance of the modified edge-converted machine learning model to operate on the first sensor data stream and produce the stream of first inferences in place of the edge-converted machine learning model.