10. A method for hybrid approach to natural language understanding, comprising the steps of: (a) creating a new natural language understanding model based at least in part by a plurality of user-provided examples using a machine learning service; (b) providing a natural language understanding model dataset comprising at least the new natural language understanding model to other components of the system using the machine learning service; (c) retrieving the natural language understanding model dataset from the machine learning service using an integrated development environment; (d) publishing a runtime solution by combining at least a natural language understanding rule and a natural language understanding model dataset using an integrated development environment; (e) retrieving the runtime solution from the integrated development environment using an interaction engine; (f) receiving a natural language input from an external interface using the interaction engine; (g) processing the natural language input by at least annotating and classifying the input using the interaction engine; (h) generating a log dataset based at least on how the runtime solution and processed input interprets the natural language input using the interaction engine; (i) storing the natural language input, and log dataset to a log storage using the interaction engine; (j) requesting the log dataset from the log storage using the machine learning service; and (k) retraining and improve the natural language understanding model dataset using the machine learning service; wherein the processing of the natural language input uses a hybrid solution comprising both natural language rules and machine learning-based classifiers.
10. A method for hybrid approach to natural language understanding, comprising the steps of: (a) creating a new natural language understanding model based at least in part by a plurality of user-provided examples using a machine learning service; (b) providing a natural language understanding model dataset comprising at least the new natural language understanding model to other components of the system using the machine learning service; (c) retrieving the natural language understanding model dataset from the machine learning service using an integrated development environment; (d) publishing a runtime solution by combining at least a natural language understanding rule and a natural language understanding model dataset using an integrated development environment; (e) retrieving the runtime solution from the integrated development environment using an interaction engine; (f) receiving a natural language input from an external interface using the interaction engine; (g) processing the natural language input by at least annotating and classifying the input using the interaction engine; (h) generating a log dataset based at least on how the runtime solution and processed input interprets the natural language input using the interaction engine; (i) storing the natural language input, and log dataset to a log storage using the interaction engine; (j) requesting the log dataset from the log storage using the machine learning service; and (k) retraining and improve the natural language understanding model dataset using the machine learning service; wherein the processing of the natural language input uses a hybrid solution comprising both natural language rules and machine learning-based classifiers. 16. The method of claim 10 , wherein data from the log storage is processed by the machine learning service determine data that is usable for a self-learning process, and use the data to automatically retrain available natural language understanding models.